Pith. sign in

REVIEW 3 major objections 4 minor 70 references

Leveraging Spatial Cues from Cochlear Implant Microphones to Efficiently Enhance Speech Separation in Real-World Listening Scenes

T0 review · 3 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Spatial cues from cochlear implant microphones—both learned from multi-channel waveforms and explicitly added as inter-microphone phase differences—substantially improve speech separation in real-world reverberant scenes, with explicit…

desk verdict Solid empirical study of spatial cues for CI speech separation, but the single-CI benefit claim is confounded by computing IPDs from both ears. read the letter →

arxiv 2501.14610 v1 pith:WV4DSJAS submitted 2025-01-24 cs.SD cs.AIeess.AS

Feyisayo Olalere , Kiki van der Heijden , Christiaan H. Stronks , Jeroen Briaire , Johan HM Frijns , Marcel van Gerven This is my paper · ORCID
classification cs.SDcs.AIeess.AS
keywords speechseparationcochlearimplantsspatialcuesinter-microphonephasedifferencereverberationreal-worldacousticscenestime-domaindeeplearningbinauralprocessing
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Speech separation models that work on clean, single-channel recordings lose most of their performance when moved into real rooms, where reverberation and multiple talkers at different positions create a much harder problem. This paper argues that the spatial information already available in cochlear implant microphone recordings can recover much of that loss. Training a time-domain separation network on two-microphone input—either two microphones on one implant or one microphone on each ear—lets the model use implicit spatial cues, and explicitly adding inter-microphone phase differences (IPDs) helps most when those implicit cues are weak. The authors report that even a single cochlear implant benefits from such explicit cues, and that spatial cues matter most when talkers have similar voices. If these results hold, cochlear implant front-ends should be built to take multi-channel input and should add IPD features to improve separation in everyday listening scenes.

What carries the argument

The load-bearing machinery is the comparison of seven input configurations for a fixed, efficient time-domain model. The model is SuDoRM-RF, an encoder–separator–decoder network that estimates each talker's waveform directly from the mixture. Implicit spatial cues are whatever the network can learn from the waveform itself: one channel, two channels on the same implant, or two channels on opposite ears. Explicit spatial cues are inter-microphone phase differences, computed as IPD(t,f) = angle(y_c1(t,f)/y_c2(t,f)) from the short-time Fourier transforms of two channels, mean-normalized, and concatenated to the encoded mixture before separation. The paper constructs its argument from the pattern of SI-SDRi, STOI, and PESQ improvements across these seven configurations and from statistical tests of how separation angle and talker gender modulate those improvements.

What would settle it

Run a controlled experiment that trains the same model on a single implant's microphone pair for IPD extraction; if the improvement over 27.01 dB SI-SDRi disappears, the single-CI explicit-cue benefit is falsified.

Watch

Extended reading notes

Core claim

On its own terms, the paper establishes a performance ordering among seven input configurations for the SuDoRM-RF speech separation model trained on simulated two-talker mixtures in reverberant rooms. Dry, non-spatial training gives an SI-SDRi of 12.96 dB, while the same model on one-channel reverberant spatial mixtures reaches 27.01 dB SI-SDRi but with far lower absolute SI-SDR, showing that real-world acoustics change the task. Adding a second channel from the same implant improves SI-SDRi to 27.99 dB; adding a second channel from the other ear improves it to 28.05 dB. Explicit IPD cues push two-channel unilateral input to 29.41 dB and bilateral input to 28.72 dB, and the largest relative gain from IPDs occurs for the two-channel unilateral configuration (+51.8% SI-SDR). The paper interprets this as evidence that implicit cues are strong bilaterally, weaker unilaterally, and weakest for single-channel input, so explicit cues fill in exactly where implicit cues fall short. It also reports that spatial cues improve separation for same-gender talker pairs more than for mixed-gender pairs, and that spatial input helps even for talkers at the same location.

Load-bearing premise

The claim that even a single cochlear implant benefits from explicit spatial cues assumes the device has access to a microphone signal from the other ear, because the IPD features are computed between the left-ear and right-ear channels.

Editorial extensions

If this is right

  • Cochlear implant front-ends should take multi-channel waveform input: two channels from opposite ears improve separation even without explicit features, raising SI-SDRi from 27.01 dB (one channel) to 28.05 dB while keeping the same 2.6M-parameter model.
  • Explicit IPD features should be added when implicit spatial cues are weak, above all for two microphones on a single implant, where they produce the largest relative gain (+51.8% SI-SDR) and the best overall SI-SDRi (29.41 dB).
  • Speech separation models for assistive devices need training on real-world spatialized, reverberant data: the same model drops 79.4% in SI-SDR when moved from dry non-spatial mixtures to real-world scenes.
  • Spatial cues are especially valuable for same-gender talker pairs, whose similar voices make spectral separation ambiguous; gains from spatial separation are roughly two to three times larger than for mixed-gender pairs.
  • Spatial input helps even when talkers overlap spatially, implying the model learns frequency-specific reverberation structure from separated training talkers and applies it to co-located mixtures.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: because the IPD features are computed between a left-ear channel and a right-ear channel, the 'single CI benefits from explicit cues' result actually assumes access to the contralateral ear's microphone signal; a device with only one physical implant would need IPDs computed from two microphones on the same implant, and it remains untested whether those convey enough spatial i
  • Editorial inference: the 46% parameter increase and the STFT needed for IPD extraction mean explicit cues are not free; a practical front-end might use implicit cues by default and enable IPD processing only when channel coherence or localization confidence is low.
  • Editorial inference: a direct test of the paper's logic would retrain the same model with IPDs from the front and back microphones of a single CI; if the improvement over 27.01 dB SI-SDRi vanishes, the single-CI conclusion reduces to a bilateral-streaming result.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper adapts the SuDoRM-RF time-domain speech separation model to multi-channel input and to auxiliary inter-microphone phase difference (IPD) features, and trains it on simulated reverberant two-talker mixtures rendered with cochlear-implant-specific head-related impulse responses. Seven input configurations are compared, ranging from one-channel unilateral waveforms to bilateral two-channel waveforms, with and without explicit IPD features. The reported results show substantial SI-SDRi/STOI/PESQ gains in spatial scenes and a larger effect of IPD for two-channel unilateral input; the authors conclude that implicit and explicit spatial cues improve separation, especially for same-gender talkers, and that even single CIs benefit from explicit cues.

Significance. The question is practically relevant for CI front-end design, and the paper's strengths include a realistic simulation pipeline (500 rooms, CI-HRTFs from a manufacturer), a reproducible public code repository, standard evaluation metrics, and a systematic ablation of input configurations. The finding that spatial cues help most when spectral cues are ambiguous (same-gender talkers) is an interesting, falsifiable result with a clear mechanism. However, the key unilateral-CI conclusion is undermined by the bilateral construction of the explicit IPD features, and the cross-configuration comparisons lack statistical support, so the significance of the headline claim is currently limited.

major comments (3)
  1. [III-A, Fig. 3B, Table I rows 5-6] The unilateral configurations with explicit cues do not use unilateral data. Section III-A states that for the three explicit configurations 'we calculated IPDs between the bilateral channels, that is, a channel from the left-ear CI and a channel from the right-ear CI.' Therefore the 'one-channel unilateral waveform + IPD' model receives an interaural phase difference from the contralateral CI, and the 'two-channel unilateral waveform + IPD' model receives an IPD from the opposite ear rather than from the two microphones on the same device. A unilateral CI user has no access to the contralateral channel. The improved results in Table I rows 5 and 6 therefore conflate adding an explicit feature with adding a second-ear signal, and the abstract's claim that 'even single CIs benefit from explicit cues' is not supported by the configuration as described. The paper should either restrict the 'unilateral + IPD' conditions to IPDs computed from two microphones on the same CI (e.g., T-mic and back-mic), or relabel the conditions as bilateral-augmented and drop the unilateral conclusion.
  2. [IV-C, Tables I-III] The paper's central comparative claim—that explicit cues are particularly beneficial when implicit cues are weak—is supported only by point-estimate differences in Table I. The Kruskal-Wallis tests in Tables II and III compare performance across spatial angles within a single input configuration; they never test whether the 'with IPD' model significantly outperforms the 'without IPD' model on the same test set, nor whether the interaction between input configuration and IPD benefit is significant. Given the modest absolute differences (e.g., STOI 0.73 vs 0.78) and no reported variance or multiple-seed information, a paired test across test utterances (or bootstrapped confidence intervals) is needed to establish the claimed ordering of benefits.
  3. [I, II-C, III-A] The paper motivates the work by efficiency and low latency for CI front-ends, and the title emphasizes efficient enhancement, but no computational cost is measured. Adding IPD increases parameter count by 46% (Table I) and requires an STFT with 512 frequency bins, yet runtime, FLOPs, or memory usage are not reported. Either include such measurements or temper the efficiency claim to parameter count and model-size considerations.
minor comments (4)
  1. [IV-E, Table III] Section IV-E reports a 23.0% improvement for two-male mixtures and 12.0% for two-female mixtures under the Two-channel, unilateral + IPD configuration, but Table III shows the reverse (F-F 23.02%, M-M 12.00%); please correct the discrepancy.
  2. [Throughout] Typos: 'auxilliary' appears in Sections I, III-A, and V; 'Kruskall-Wallis' appears in Section IV-E and Table III; and 'Fig. 4B)' is missing a space before the subsequent sentence.
  3. [Fig. 3B] Figure 3B is difficult to parse at the level of detail needed to verify which channels feed the IPD computation; a table listing the exact channel pair used for IPD for each configuration would make the setup unambiguous.
  4. [II-B2, VII] The description of the CI-HRTF data and the GitHub repository would benefit from explicit version identifiers or access dates, so that the exact microphone configuration and impulse-response set can be reproduced.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's claims are empirical results from trained models evaluated on a fixed test set, with no derivation that reduces to its inputs.

full rationale

This paper is an experimental study. It trains a SuDoRM-RF model on several input configurations (single-channel, two-channel unilateral, two-channel bilateral, with and without explicit IPD features) and measures SI-SDR, SI-SDRi, STOI, and PESQ on a held-out test set. The central claims are comparative performance statements drawn from Table I and subsequent statistical analyses. There is no fitted parameter that is later renamed as a prediction, no uniqueness theorem imported from the authors' prior work, and no ansatz smuggled in via self-citation. The only potentially concerning point is that the 'one-channel unilateral + IPD' configuration computes IPDs between a left-ear CI channel and a right-ear CI channel, meaning the explicit cue is bilateral even though the waveform input is unilateral. This is a validity or ecological-validity concern about whether the experiment supports the 'single CI benefits from explicit cues' conclusion, but it is not circularity: the IPD feature is not defined in terms of the separation output, and the result does not reduce by construction to the input. The paper is self-contained against external benchmarks (WSJ0-2mix, SuDoRM-RF as a prior architecture) and does not rely on self-citation for its load-bearing empirical claims. Accordingly, the circularity score is 0.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The paper makes no derivation; it reports empirical model comparisons. The ledger captures the experimental design choices that shape the dataset and the domain assumptions about ecological validity, plus one ad hoc assumption that is structurally important for the single-CI conclusion.

free parameters (4)
  • T60 sampling range = 0.2 to 0.7 s
    Hand-chosen to mimic classrooms, meeting rooms, and restaurants; not fitted to a target but shapes task difficulty.
  • T60-room size scaling = scaled room size mapped to T60
    Introduced to create a naturalistic relation between room size and reverberation; arbitrary mapping.
  • SNR range = 0 to 5 dB
    Hand-chosen range for mixing two talkers.
  • IPD STFT parameters = window=512 samples, hop=8 samples, bins=512
    Chosen to match the encoder's frequency features; affects the spatial feature.
assumptions (4)
  • domain assumption CI-HRTFs measured on a KEMAR mannequin with Advanced Bionics CIs are representative of real CI users' acoustic behavior.
    Used without validation against real CI users; Section II-B2.
  • domain assumption Simulated shoebox rooms with image-source RIRs and T60 in [0.2,0.7] s adequately represent real-world listening scenes.
    Section II-B1; the realism of the simulation is not independently verified.
  • ad hoc to paper A unilateral CI user with one audio channel can use an IPD computed from bilateral microphones.
    Section III-A; this is the flawed assumption behind the single-CI + explicit cue claim.
  • domain assumption Dry speech targets are the right training target and results transfer to CI user benefit.
    Section III-B; they justify it but do not test with CI listeners.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Leveraging Spatial Cues from Cochlear Implant Microphones to Efficiently Enhance Speech Separation in Real-World Listening Scenes." pith.science (2026). https://pith.science/paper/WV4DSJAS

@misc{pith2026250114610,
  author       = {Pith},
  title        = {Pith review of: Leveraging Spatial Cues from Cochlear Implant Microphones to Efficiently Enhance Speech Separation in Real-World Listening Scenes},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WV4DSJAS}},
  note         = {Machine review of arXiv:2501.14610}
}
read the original abstract

Speech separation approaches for single-channel, dry speech mixtures have significantly improved. However, real-world spatial and reverberant acoustic environments remain challenging, limiting the effectiveness of these approaches for assistive hearing devices like cochlear implants (CIs). To address this, we quantify the impact of real-world acoustic scenes on speech separation and explore how spatial cues can enhance separation quality efficiently. We analyze performance based on implicit spatial cues (inherent in the acoustic input and learned by the model) and explicit spatial cues (manually calculated spatial features added as auxiliary inputs). Our findings show that spatial cues (both implicit and explicit) improve separation for mixtures with spatially separated and nearby talkers. Furthermore, spatial cues enhance separation when spectral cues are ambiguous, such as when voices are similar. Explicit spatial cues are particularly beneficial when implicit spatial cues are weak. For instance, single CI microphone recordings provide weaker implicit spatial cues than bilateral CIs, but even single CIs benefit from explicit cues. These results emphasize the importance of training models on real-world data to improve generalizability in everyday listening scenarios. Additionally, our statistical analyses offer insights into how data properties influence model performance, supporting the development of efficient speech separation approaches for CIs and other assistive devices in real-world settings.

Figures

Figures reproduced from arXiv: 2501.14610 by the authors.

Figure 1
Figure 1. (A) Schematic depiction of data generation. CI-HRIR [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. (A) Reverberation time (T60) as a function of room size. Each circle represents a single room. (B) Effect of reverberation and spatialization on speech mixtures. Top panes show waveforms of a dry, non-spatial speech mixture and a spatial, reverberant version of the same speech mixture. For illustration, bottom panes depict spectrograms (but note that models are trained directly on the waveform). (C) Presence of impl… view at source ↗
Figure 3
Figure 3. (A) SuDoRM-RF [24] implementation in present study. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Speech separation performance as a function of spati [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: SI-SDR as a function of talker gender pairing and [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

70 extracted references · 67 canonical work pages

  1. [1]

    Deafness and Hearing

    World Health Organization (WHO), “Deafness and Hearing .” https://www.who.int/news-room/fact-sheets/detail/deafness-and-hearing-loss,

    work page

  2. [2]

    Speech perception in nois e by children with cochlear implants,

    A. Caldwell and S. Nittrouer, “Speech perception in nois e by children with cochlear implants,” 2013

    work page 2013

  3. [3]

    Cochlear implant,

    J. G. Naples and M. J. Ruckenstein, “Cochlear implant,” Otolaryngologic Clinics of North America , vol. 53, no. 1, pp. 87–102, 2020

    work page 2020

  4. [4]

    Effects of simulated cochlea r-implant processing on speech reception in fluctuating maskers,

    M. K. Qin and A. J. Oxenham, “Effects of simulated cochlea r-implant processing on speech reception in fluctuating maskers,” The Journal of the Acoustical Society of America , vol. 114, no. 1, pp. 446–454, 2003

    work page 2003

  5. [5]

    Cochlear implant speech recognition with speech maskers,

    G. S. Stickney, F.-G. Zeng, R. Litovsky, and P . Assmann, “ Cochlear implant speech recognition with speech maskers,” The Journal of the Acoustical Society of America , vol. 116, no. 2, pp. 1081–1091, 2004

    work page 2004

  6. [6]

    Difficulty understanding spee ch in noise by the hearing impaired: Underlying causes and technological solutions,

    E. W. Healy and S. E. Y oho, “Difficulty understanding spee ch in noise by the hearing impaired: Underlying causes and technological solutions,” in 2016 38Th Annual International Conference Of The IEEE Engin eering In Medicine And Biology Society (EMBC) , pp. 89–92, IEEE, 2016

    work page 2016

  7. [7]

    Selective attentio n in normal and impaired hearing,

    B. G. Shinn-Cunningham and V . Best, “Selective attentio n in normal and impaired hearing,” Trends in Amplification , vol. 12, no. 4, pp. 283–299, 2008

    work page 2008

  8. [8]

    Listen ing in naturalistic scenes: What can functional near-infrared spectroscopy and intersubject correlation analysis tell us about the und erlying brain activity?,

    S. C. Rowland, D. E. H. Hartley, and I. M. Wiggins, “Listen ing in naturalistic scenes: What can functional near-infrared spectroscopy and intersubject correlation analysis tell us about the und erlying brain activity?,” Trends in Hearing , vol. 22, p. 2331216518804116, 2018

    work page 2018

Show all 70 references
  1. [9]

    Using blind source separ ation techniques to improve speech recognition in bilateral coch lear implant patients,

    K. Kokkinakis and P . C. Loizou, “Using blind source separ ation techniques to improve speech recognition in bilateral coch lear implant patients,” The Journal of the Acoustical Society of America , vol. 123, no. 4, pp. 2379–2390, 2008

    2008

  2. [10]

    On training targets for super- vised speech separation,

    Y . Wang, A. Narayanan, and D. Wang, “On training targets for super- vised speech separation,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 22, no. 12, pp. 1849–1858, 2014

    2014

  3. [11]

    Divide and conquer: A deep casa appro ach to talker-independent monaural speaker separation,

    Y . Liu and D. Wang, “Divide and conquer: A deep casa appro ach to talker-independent monaural speaker separation,” IEEE/ACM Transac- tions on Audio, Speech, and Language Processing , vol. 27, no. 12, pp. 2092–2102, 2019

    2019

  4. [12]

    Causal deep casa for monaural talker -independent speaker separation,

    Y . Liu and D. Wang, “Causal deep casa for monaural talker -independent speaker separation,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 28, pp. 2109–2118, 2020

    2020

  5. [13]

    Complex ratio ma sking for monaural speech separation,

    D. S. Williamson, Y . Wang, and D. Wang, “Complex ratio ma sking for monaural speech separation,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 24, no. 3, pp. 483–492, 2015

    2015

  6. [14]

    Ideal ratio mask estimation u sing deep neu- ral networks for robust speech recognition,

    A. Narayanan and D. Wang, “Ideal ratio mask estimation u sing deep neu- ral networks for robust speech recognition,” in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing , pp. 7092–7096, IEEE, 2013

    2013

  7. [15]

    Speech enhancem ent based on deep denoising autoencoder.,

    X. Lu, Y . Tsao, S. Matsuda, and C. Hori, “Speech enhancem ent based on deep denoising autoencoder.,” in Interspeech, vol. 2013, pp. 436–440, 2013

    2013

  8. [16]

    An experimental s tudy on speech enhancement based on deep neural networks,

    Y . Xu, J. Du, L.-R. Dai, and C.-H. Lee, “An experimental s tudy on speech enhancement based on deep neural networks,” IEEE Signal Processing Letters, vol. 21, no. 1, pp. 65–68, 2013

    2013

  9. [17]

    A fast g riffin-lim algo- rithm,

    N. Perraudin, P . Balazs, and P . L. Søndergaard, “A fast g riffin-lim algo- rithm,” in 2013 IEEE W orkshop on Applications of Signal Processing to Audio and Acoustics , pp. 1–4, IEEE, 2013

    2013

  10. [18]

    Neural spatial filter: Target speaker speech separat ion assisted with directional information.,

    R. Gu, L. Chen, S.-X. Zhang, J. Zheng, Y . Xu, M. Y u, D. Su, Y . Zou, and D. Y u, “Neural spatial filter: Target speaker speech separat ion assisted with directional information.,” in Interspeech, pp. 4290–4294, 2019

    2019

  11. [19]

    Supervised speech separation base d on deep learning: An overview,

    D. Wang and J. Chen, “Supervised speech separation base d on deep learning: An overview,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 26, no. 10, pp. 1702–1726, 2018

    2018

  12. [20]

    Deep learning for monaural speech separation,

    P .-S. Huang, M. Kim, M. Hasegawa-Johnson, and P . Smarag dis, “Deep learning for monaural speech separation,” in 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICA SSP), pp. 1562–1566, IEEE, 2014

    2014

  13. [21]

    A deep ensemble learning metho d for monaural speech separation,

    X.-L. Zhang and D. Wang, “A deep ensemble learning metho d for monaural speech separation,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 24, no. 5, pp. 967–977, 2016

    2016

  14. [22]

    Conv-tasnet: Surpassing idea l time– frequency magnitude masking for speech separation,

    Y . Luo and N. Mesgarani, “Conv-tasnet: Surpassing idea l time– frequency magnitude masking for speech separation,” IEEE/ACM Trans- actions on Audio, Speech, and Language Processing , vol. 27, no. 8, pp. 1256–1266, 2019

    2019

  15. [23]

    Dual-path RNN: efficie nt long sequence modeling for time-domain single-channel speech s eparation,

    Y . Luo, Z. Chen, and T. Y oshioka, “Dual-path RNN: efficie nt long sequence modeling for time-domain single-channel speech s eparation,” in ICASSP 2020-2020 IEEE International Conference on Acousti cs, Speech and Signal Processing (ICASSP) , pp. 46–50, IEEE, 2020

    2020

  16. [24]

    Compute and memory efficient universal sound source separation,

    E. Tzinis, Z. Wang, X. Jiang, and P . Smaragdis, “Compute and memory efficient universal sound source separation,” vol. 94, no. 2 , pp. 245–259, 2022

    2022

  17. [25]

    Temporal-spatial neural filter: Direc tion informed end-to-end multi-channel target speech separation,

    R. Gu and Y . Zou, “Temporal-spatial neural filter: Direc tion informed end-to-end multi-channel target speech separation,” ArXiv Preprint ArXiv:2001.00391, 2020

    2001 arXiv

  18. [26]

    Whamr!: Noisy and reverberant single-channel speech separation,

    M. Maciejewski, G. Wichern, E. McQuinn, and J. Le Roux, “ Whamr!: Noisy and reverberant single-channel speech separation,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Spe ech and Signal Processing (ICASSP) , pp. 696–700, IEEE, 2020

    2020

  19. [27]

    Mossformer2: Combining transforme r and RNN-Free recurrent network for enhanced time-domain mo naural speech separation,

    S. Zhao, Y . Ma, C. Ni, C. Zhang, H. Wang, T. H. Nguyen, K. Zh ou, J. Yip, D. Ng, and B. Ma, “Mossformer2: Combining transforme r and RNN-Free recurrent network for enhanced time-domain mo naural speech separation,” ArXiv Preprint ArXiv:2312.11825 , 2023

    2023 arXiv

  20. [28]

    Multi-channel overlapped speech recognition with locati on guided speech extraction network,

    Z. Chen, X. Xiao, T. Y oshioka, H. Erdogan, J. Li, and Y . Go ng, “Multi-channel overlapped speech recognition with locati on guided speech extraction network,” in 2018 IEEE Spoken Language Technology W orkshop (SLT), pp. 558–565, IEEE, 2018

    2018

  21. [29]

    Combining spectral and spatial features for deep learning based blind speaker separation,

    Z.-Q. Wang and D. Wang, “Combining spectral and spatial features for deep learning based blind speaker separation,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 27, no. 2, pp. 457– 468, 2018

    2018

  22. [30]

    Enhancing end-to-end multi-channel speech separation vi a spatial fea- ture learning,

    R. Gu, S.-X. Zhang, L. Chen, Y . Xu, M. Y u, D. Su, Y . Zou, and D. Y u, “Enhancing end-to-end multi-channel speech separation vi a spatial fea- ture learning,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pp. 7319–732...

    2020

  23. [31]

    3d spatial features for multi- channel target speech separation,

    R. Gu, S.-X. Zhang, M. Y u, and D. Y u, “3d spatial features for multi- channel target speech separation,” in 2021 IEEE Automatic Speech Recognition and Understanding W orkshop (ASRU), pp. 996–1002, IEEE, 2021

    2021

  24. [32]

    Real-time binaural sp eech sep- aration with preserved spatial cues,

    C. Han, Y . Luo, and N. Mesgarani, “Real-time binaural sp eech sep- aration with preserved spatial cues,” in ICASSP 2020-2020 IEEE In- ternational Conference on Acoustics, Speech and Signal Pro cessing (ICASSP), pp. 6404–6408, IEEE, 2020

    2020

  25. [33]

    Binaural speaker localiza tion and sepa- ration based on a joint itd/ild model and head movement track ing,

    M. Zohourian and R. Martin, “Binaural speaker localiza tion and sepa- ration based on a joint itd/ild model and head movement track ing,” in 2016 IEEE International Conference on Acoustics, Speech an d Signal Processing (ICASSP), pp. 430–434, IEEE, 2016

    2016

  26. [34]

    Recovering speech intell igibility with deep learning and multiple microphones in noisy-reverberant si tuations for people using cochlear implants,

    C. Gaultier and T. Goehring, “Recovering speech intell igibility with deep learning and multiple microphones in noisy-reverberant si tuations for people using cochlear implants,” The Journal of the Acoustical Society of America , vol. 155, no. 6, pp. 3833–3847, 2024

    2024

  27. [35]

    Deep clustering: Discriminative embeddings for segmentation and separatio n,

    J. R. Hershey, Z. Chen, J. Le Roux, and S. Watanabe, “Deep clustering: Discriminative embeddings for segmentation and separatio n,” in 2016 IEEE International Conference on Acoustics, Speech and Sig nal pro- cessing (ICASSP) , pp. 31–35, IEEE, 2016

    2016

  28. [36]

    Pyroomacou stics: A python package for audio room simulation and array processing algo rithms,

    R. Scheibler, E. Bezzam, and I. Dokmani´ c, “Pyroomacou stics: A python package for audio room simulation and array processing algo rithms,” in 2018 IEEE International Conference on Acoustics, Speech an d Signal Processing (ICASSP), pp. 351–355, IEEE, 2018. 10

    2018

  29. [37]

    Reverberation time graphic

    Commercial Acoustics, “Reverberation time graphic.” https://commercial-acoustics.com/reverberation-time-graphic/, 2023. Accessed on June 5, 2023

    2023

  30. [38]

    Reverberation time in room acoustics

    Larson Davis, “Reverberation time in room acoustics.” http://www.larsondavis.com/learn/building-acoustics/Reverberation-Time-in-Room-Acoustics,

  31. [39]

    Understanding small room reverberation time measurements

    Acoustic Frontiers, “Understanding small room reverberation time measurements.” https://acousticfrontiers.com/blogs/articles/understanding-small-room-reverberation-time-measurements,

  32. [40]

    Optimizations of the spatial decomposition method for bin aural repro- duction,

    S. V . Amengual Gar´ ı, J. M. Arend, P . T. Calamia, and P . W. Robinson, “Optimizations of the spatial decomposition method for bin aural repro- duction,” Journal of the Audio Engineering Society , vol. 68, no. 12, pp. 959–976, 2021

    2021

  33. [41]

    Hrft measurements of a kemar dummy- head microphone,

    B. Gardner, K. Martin, et al. , “Hrft measurements of a kemar dummy- head microphone,” 1994

    1994

  34. [42]

    Accessed on June 5, 2023

    2023

  35. [43]

    Cochlear implant microphone location affec ts speech recognition in diffuse noise,

    E. R. Kolberg, S. W. Sheffield, T. J. Davis, L. W. Sunderha us, and R. H. Gifford, “Cochlear implant microphone location affec ts speech recognition in diffuse noise,” Journal of the American Academy of Audiology, vol. 26, no. 01, pp. 051–058, 2015

    2015

  36. [44]

    T-mic microphone

    “T-mic microphone.” https://www.advancedbionics.c om/nl/nl/home/solutions/accessories/t-mic.html. Accessed: January 31, 2024

    2024

  37. [45]

    The effect of mic rophone placement on interaural level differences and sound locali zation across the horizontal plane in bilateral cochlear implant users,

    H. G. Jones, A. Kan, and R. Y . Litovsky, “The effect of mic rophone placement on interaural level differences and sound locali zation across the horizontal plane in bilateral cochlear implant users,” Ear and hearing, vol. 37, no. 5, pp. e341–e345, 2016

    2016

  38. [46]

    Flexible binaural resynthesis of room impulse responses f or augmented reality research,

    S. V . A. Gar´ ı, W. O. Brimijoin, H. G. Hassager, and P . W. R obinson, “Flexible binaural resynthesis of room impulse responses f or augmented reality research,” in EAA Spatial Audio Signal Processing Symposium , pp. 161–166, 2019

    2019

  39. [47]

    Spati al decomposition method for room impulse responses,

    S. Tervo, J. P¨ atynen, A. Kuusinen, and T. Lokki, “Spati al decomposition method for room impulse responses,” Journal of the Audio Engineering Society, vol. 61, no. 1/2, pp. 17–28, 2013

    2013

  40. [48]

    Acoustic factors affectin g interaural level differences for cochlear-implant users,

    P . G. Mayo and M. J. Goupell, “Acoustic factors affectin g interaural level differences for cochlear-implant users,” The Journal of the Acoustical Society of America , vol. 147, no. 4, pp. EL357–EL362, 2020

    2020

  41. [49]

    Two-dimensional s ound local- ization by human listeners,

    J. C. Makous and J. C. Middlebrooks, “Two-dimensional s ound local- ization by human listeners,” The journal of the Acoustical Society of America, vol. 87, no. 5, pp. 2188–2200, 1990

    1990

  42. [50]

    Computational audiology: new approaches to adva nce hearing health care in the digital age,

    J.-W. A. Wasmann, C. P . Lanting, W. J. Huinck, E. A. Mylan us, J. W. van der Laak, P . J. Govaerts, D. W. Swanepoel, D. R. Moore, and D. L. Barbour, “Computational audiology: new approaches to adva nce hearing health care in the digital age,” Ear and Hearing, vol. 42, no. 6, ...

    2021

  43. [51]

    Acuity of sound localisa tion: a topography of auditory space. i. normal hearing conditions ,

    S. R. Oldfield and S. P . Parker, “Acuity of sound localisa tion: a topography of auditory space. i. normal hearing conditions ,” Perception, vol. 13, no. 5, pp. 581–600, 1984

    1984

  44. [52]

    Sdr –half-baked or well done?,

    J. Le Roux, S. Wisdom, H. Erdogan, and J. R. Hershey, “Sdr –half-baked or well done?,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pp. 626–630, IEEE, 2019

    2019

  45. [53]

    Adam: A method for stochastic opt imization,

    D. P . Kingma and J. Ba, “Adam: A method for stochastic opt imization,” ArXiv Preprint ArXiv:1412.6980 , 2014

    2014 arXiv

  46. [54]

    Pytorch: An imperative style, high-performance deep learning library ,

    A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. C hanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al. , “Pytorch: An imperative style, high-performance deep learning library ,” Advances in n=Neural Information Processing Systems , vol. 32, 2019

    2019

  47. [55]

    Asteroid : the PyTorch-based audio source separation toolkit for researc hers,

    M. Pariente, S. Cornell, J. Cosentino, S. Sivasankaran , E. Tzinis, J. Heitkaemper, M. Olvera, F.-R. St¨ oter, M. Hu, J. M. Mart´ ı n-Do˜ nas, D. Ditter, A. Frank, A. Deleforge, and E. Vincent, “Asteroid : the PyTorch-based audio source separation toolkit for researc hers,” in...

    2020

  48. [56]

    A n algorithm for intelligibility prediction of time–frequency weighte d noisy speech,

    C. H. Taal, R. C. Hendriks, R. Heusdens, and J. Jensen, “A n algorithm for intelligibility prediction of time–frequency weighte d noisy speech,” IEEE Transactions on Audio, Speech, and Language Processin g, vol. 19, no. 7, pp. 2125–2136, 2011

    2011

  49. [57]

    Permutation invariant training of deep models for speaker-independent multi-tal ker speech sep- aration,

    D. Y u, M. Kolbæk, Z.-H. Tan, and J. Jensen, “Permutation invariant training of deep models for speaker-independent multi-tal ker speech sep- aration,” in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pp. 241–245, IEEE, 2017

    2017

  50. [58]

    Torchaudio: Building blocks for audio and speech processing,

    Y .-Y . Y ang, M. Hira, Z. Ni, A. Astafurov, C. Chen, C. Puhr sch, D. Pollack, D. Genzel, D. Greenberg, E. Z. Y ang, et al. , “Torchaudio: Building blocks for audio and speech processing,” in ICASSP 2022- 2022 IEEE International Conference on Acoustics, Speech an d Signal Proc...

    2022

  51. [59]

    Attention is all you need in speech separation,

    C. Subakan, M. Ravanelli, S. Cornell, M. Bronzi, and J. Z hong, “Attention is all you need in speech separation,” in ICASSP 2021- 2021 IEEE International Conference on Acoustics, Speech an d Signal Processing (ICASSP), pp. 21–25, IEEE, 2021

    2021

  52. [60]

    Perceptual evaluation of speech quality (pesq)-a new method for speech quality as- sessment of telephone networks and codecs,

    A. W. Rix, J. G. Beerends, M. P . Hollier, and A. P . Hekstra , “Perceptual evaluation of speech quality (pesq)-a new method for speech quality as- sessment of telephone networks and codecs,” in 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Pr...

    2001

  53. [61]

    Pas t review, current progress, and challenges ahead on the cocktail part y problem,

    Y .-m. Qian, C. Weng, X.-k. Chang, S. Wang, and D. Y u, “Pas t review, current progress, and challenges ahead on the cocktail part y problem,” Frontiers of Information Technology & Electronic Engineer ing, vol. 19, pp. 40–63, 2018

    2018

  54. [62]

    Cortical mechanisms of spatial hearing,

    K. van der Heijden, J. P . Rauschecker, B. de Gelder, and E . Formisano, “Cortical mechanisms of spatial hearing,” Nature Reviews Neuroscience, vol. 20, no. 10, pp. 609–623, 2019

    2019

  55. [63]

    Controlling the false discovery r ate: a practical and powerful approach to multiple testing,

    B. Y oav and H. Y osef, “Controlling the false discovery r ate: a practical and powerful approach to multiple testing,” Journal of the Royal Statistical Society: Series B (Methodological) , vol. 57, no. 1, pp. 289– 300, 1995

    1995

  56. [64]

    Monaural and binaural s peech perception through hearing aids under noise and reverberat ion with normal and hearing-impaired listeners,

    A. K. Nabelek and J. M. Pickett, “Monaural and binaural s peech perception through hearing aids under noise and reverberat ion with normal and hearing-impaired listeners,” Journal of Speech and Hearing Research, vol. 17, no. 4, pp. 724–739, 1974

    1974

  57. [65]

    Eff ects of reverberation and noise on speech intelligibility in norma l-hearing and aided hearing-impaired listeners,

    J. Xia, B. Xu, S. Pentony, J. Xu, and J. Swaminathan, “Eff ects of reverberation and noise on speech intelligibility in norma l-hearing and aided hearing-impaired listeners,” The Journal of the Acoustical Society of America , vol. 143, no. 3, pp. 1523–1533, 2018

    2018

  58. [66]

    Estimation of the frequency d ependent reverberation time by means of warped filter-banks,

    H. L ¨ ollmann and P . V ary, “Estimation of the frequency d ependent reverberation time by means of warped filter-banks,” pp. 309 –312, IEEE, 2011

    2011

  59. [67]

    Blind speech separa tion and derever- beration using neural beamforming,

    L. Pfeifenberger and F. Pernkopf, “Blind speech separa tion and derever- beration using neural beamforming,” Speech Communication, vol. 140, pp. 29–41, 2022

    2022

  60. [68]

    Wpd++: An improved neural beamformer for simultaneous spe ech separation and dereverberation,

    Z. Ni, Y . Xu, M. Y u, B. Wu, S. Zhang, D. Y u, and M. I. Mandel, “Wpd++: An improved neural beamformer for simultaneous spe ech separation and dereverberation,” in 2021 IEEE Spoken Language Tech- nology W orkshop (SLT), pp. 817–824, IEEE, 2021

    2021

  61. [69]

    Multi- channel signal enhancement algorithms for assisted listen ing devices: Exploiting spatial diversity using multiple microphones,

    S. Doclo, W. Kellermann, S. Makino, and S. E. Nordholm, “ Multi- channel signal enhancement algorithms for assisted listen ing devices: Exploiting spatial diversity using multiple microphones, ” IEEE Signal Processing Magazine, vol. 32, no. 2, pp. 18–30, 2015

    2015

  62. [2021]

    [Online; accessed 14-November-2022]

    2022

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.