pith. sign in

arxiv: 2605.19984 · v1 · pith:RZU4XVEVnew · submitted 2026-05-19 · 💻 cs.SD

A conceptual framework for learning to listen by reward: Curiosity-driven search for novel sources

Andreas Triantafyllopoulos , Jakub \v{S}\v{t}astn\'y , Alexios Terpinas , Tianyi Liu , Yuanqi Wang , Bj\"orn W. Schuller This is my paper

Pith reviewed 2026-05-20 04:19 UTC · model grok-4.3

classification 💻 cs.SD
keywords reinforcement learningaudio processingcuriosity-driven explorationnovel sound sourcesunsupervised learningsound perceptionreward-based learning
0
0 comments X

The pith

Agents learn to listen through reinforcement by continuously hunting for novel sound sources.

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

Reinforcement learning succeeds by using high-level rewards instead of detailed labels, yet audio has seen little progress with this approach. The paper offers a conceptual framework in which an agent learns listening skills by receiving reward for discovering new sound sources in its surroundings. If the idea holds, agents could acquire audio understanding without any labeled training data. The authors review earlier efforts, lay out the framework, note remaining technical hurdles, and supply a proof-of-concept implementation to illustrate that the method is workable. The proposal matters because audio recordings are plentiful while manual labeling remains costly and limited.

Core claim

The paper claims that a conceptual framework centered on the continuous search for novel sound sources supplies an intrinsic reward signal sufficient for agents to learn listening behaviors in a reinforcement-learning setting, without requiring granular labels or external supervision.

What carries the argument

Continuous search for novel sound sources, which functions as the intrinsic reward that drives the acquisition of listening skills.

If this is right

  • Audio systems could be trained in unlabeled, real-world acoustic environments.
  • Learning becomes possible in settings where sound sources are dynamic and previously unknown.
  • The framework reduces dependence on large labeled audio datasets.
  • Open technical challenges in reward formulation and exploration efficiency must still be solved.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same novelty-search principle could be tested in multi-modal settings that combine audio with vision or touch.
  • Agents using this reward might adapt more readily to new acoustic conditions than models trained on fixed datasets.
  • Scaling the approach to long-duration recordings would require efficient ways to detect and remember novel sources.

Load-bearing premise

That rewarding an agent solely for finding new sound sources supplies enough guidance to produce useful listening abilities without any other supervision.

What would settle it

An experiment in which an agent trained only on novelty rewards shows no measurable improvement on downstream audio tasks such as source separation or classification relative to an agent that receives random rewards.

Figures

Figures reproduced from arXiv: 2605.19984 by Alexios Terpinas, Andreas Triantafyllopoulos, Bj\"orn W. Schuller, Jakub \v{S}\v{t}astn\'y, Tianyi Liu, Yuanqi Wang.

Figure 1
Figure 1. Figure 1: Overview of our conceptual framework. An agent is searching for [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Optimal action (arg max(fQ(sk)) for random (left) vs trained model (right). Arrows designate the direction in which the agent would move if it reached a particular point in the grid. Source designated by red dot; red circle is the radius within which the source is considered found. Green dashed lines indicate quadrant left out for evaluation. Right panel shows one particular trajectory. Simulation software… view at source ↗
read the original abstract

Reinforcement learning is a powerful learning paradigm that has spearheaded progress in numerous domains. Its core promise lies in learning through high-level goals without the need for granular labels. However, it still remains elusive in the realm of audio, where it has received substantially less attention than in computer vision or other domains. The key question remains: how can agents learn to listen purely via reward-driven exploration? In this contribution, we present an overview of previous attempts and a new conceptual framework for learning to listen by reward. Our approach depends on the continuous search for novel sound sources. We formulate our framework, discuss open technical challenges, and present a first proof-of-concept implementation that showcases the feasibility of our approach.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript reviews prior attempts at reinforcement learning for audio tasks and proposes a conceptual framework for unsupervised 'learning to listen' driven by reward from continuous curiosity-based search for novel sound sources. It formulates the framework, identifies open technical challenges, and presents a proof-of-concept implementation intended to demonstrate basic feasibility.

Significance. If the circularity between novelty quantification and learned audio representations can be resolved, the framework could supply a label-free, exploration-driven paradigm for auditory learning that parallels successful curiosity methods in vision and control, with potential impact on unsupervised audio understanding and embodied agents.

major comments (2)
  1. [Framework section] Framework section (novelty-driven reward formulation): the central claim that continuous search for novel sound sources supplies a sufficient reward signal presupposes a mechanism to quantify novelty. Any concrete implementation (prediction error, embedding distance, or density estimation) requires an internal audio representation; the manuscript does not specify whether this representation is learned jointly, hand-crafted, or drawn from a pre-trained model, leaving the approach vulnerable to the circularity noted in the stress-test.
  2. [Proof-of-concept implementation] Proof-of-concept implementation: the manuscript states that the POC 'showcases the feasibility of our approach,' yet provides no description of the audio representation used, the novelty metric, training dynamics, quantitative metrics, or controls for collapse. This omission makes it impossible to evaluate whether the joint optimization avoids the very circularity that would undermine the framework's core promise.
minor comments (2)
  1. [Abstract and introduction] The abstract and introduction could more explicitly distinguish the proposed framework from existing curiosity-driven RL methods in other modalities to clarify the audio-specific contribution.
  2. [Open technical challenges] Open technical challenges are listed but would benefit from prioritized discussion of which must be solved before a non-circular implementation is possible.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive report. The comments correctly identify areas where additional clarity would strengthen the manuscript. We address each major comment below and have revised the manuscript to incorporate the suggested improvements while preserving the conceptual focus of the work.

read point-by-point responses

  1. Referee: [Framework section] Framework section (novelty-driven reward formulation): the central claim that continuous search for novel sound sources supplies a sufficient reward signal presupposes a mechanism to quantify novelty. Any concrete implementation (prediction error, embedding distance, or density estimation) requires an internal audio representation; the manuscript does not specify whether this representation is learned jointly, hand-crafted, or drawn from a pre-trained model, leaving the approach vulnerable to the circularity noted in the stress-test.

    Authors: We agree that the circularity between novelty quantification and the underlying audio representation is a central technical challenge. The Framework section was written at a conceptual level and explicitly flags this issue in the open challenges discussion rather than asserting a complete solution. To address the referee's point, we have revised the section to enumerate concrete options (jointly learned representations via iterative bootstrapping, initialization from pre-trained self-supervised models, or hand-crafted features as a baseline) and to emphasize that joint optimization is the intended long-term direction for avoiding circularity. These additions provide guidance without altering the high-level framework. revision: yes

  2. Referee: [Proof-of-concept implementation] Proof-of-concept implementation: the manuscript states that the POC 'showcases the feasibility of our approach,' yet provides no description of the audio representation used, the novelty metric, training dynamics, quantitative metrics, or controls for collapse. This omission makes it impossible to evaluate whether the joint optimization avoids the very circularity that would undermine the framework's core promise.

    Authors: The referee is right that the current POC description is too terse to permit independent evaluation. The implementation was deliberately minimal to illustrate basic feasibility of the reward formulation rather than to serve as a full experimental validation. In the revised manuscript we have expanded the POC section with the missing details: log-mel spectrogram input, a predictive-model novelty metric based on reconstruction error, the reinforcement-learning training loop, quantitative exploration metrics, and controls demonstrating that the agent does not collapse to trivial policies. We have also added a short discussion of how the chosen representation and metric interact with the circularity concern. revision: yes

Circularity Check

0 steps flagged

Conceptual framework introduces no self-referential derivations or fitted predictions

full rationale

The paper presents a high-level conceptual overview and framework for curiosity-driven audio learning via continuous novelty search, without any equations, parameter fitting, or quantitative derivations that could reduce to inputs by construction. No load-bearing self-citations, uniqueness theorems, or ansatzes are invoked to justify the core premise; the approach is explicitly framed as depending on an open technical challenge (novelty quantification) while acknowledging implementation difficulties. The proof-of-concept is described only as demonstrating basic feasibility rather than closing a loop or renaming prior results. This is a standard non-circular outcome for a conceptual proposal paper.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on the domain assumption that novelty search supplies a usable reward signal for audio learning; no free parameters or invented entities are explicitly quantified in the abstract.

axioms (1)
  • domain assumption Reward-driven exploration via novelty search can produce effective learning in audio domains.
    Core premise invoked to justify the framework's viability.

pith-pipeline@v0.9.0 · 5676 in / 1060 out tokens · 44484 ms · 2026-05-20T04:19:15.023352+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

36 extracted references · 36 canonical work pages · 1 internal anchor

  1. [1]

    A small negative rewardr − was given for every step where the agent failed to reach the source (−.1) and a larger one when it stepped out-of-bounds −1

    The agent was assumed to reach the source when their Euclidean distance was< .6m. A small negative rewardr − was given for every step where the agent failed to reach the source (−.1) and a larger one when it stepped out-of-bounds −1. For exploration, we used theϵ-greedy strategy, withϵ initialised at.6and gradually annealed to.95at the end of each epoch w...

    work page

  2. [2]

    R. S. Sutton and A. G. Barto,Reinforcement learning: An introduction. MIT press Cambridge, 1998, vol. 1

    work page 1998

  3. [3]

    Playing Atari with Deep Reinforcement Learning

    V . Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, “Playing atari with deep reinforcement learning,”arXiv preprint arXiv:1312.5602, 2013

    work page internal anchor Pith review Pith/arXiv arXiv 2013

  4. [4]

    Mastering the game of go with deep neural networks and tree search,

    D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V . Panneershelvam, M. Lanctot, et al., “Mastering the game of go with deep neural networks and tree search,”nature, vol. 529, no. 7587, pp. 484–489, 2016

    work page 2016

  5. [5]

    Deep reinforcement learning for autonomous driving: A survey,

    B. R. Kiran, I. Sobh, V . Talpaert, P. Mannion, A. A. Al Sallab, S. Yogamani, and P. P´erez, “Deep reinforcement learning for autonomous driving: A survey,”IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 6, pp. 4909–4926, 2021

    work page 2021

  6. [6]

    Reinforcement learning in robotics: A survey,

    J. Kober, J. A. Bagnell, and J. Peters, “Reinforcement learning in robotics: A survey,”The International Journal of Robotics Research, vol. 32, no. 11, pp. 1238–1274, 2013

    work page 2013

  7. [7]

    Formal mathematical reasoning: A new frontier in ai.arXiv preprint arXiv:2412.16075, 2024

    K. Yang, G. Poesia, J. He, W. Li, K. Lauter, S. Chaudhuri, and D. Song, “Formal mathematical reasoning: A new frontier in ai,”arXiv preprint arXiv:2412.16075, 2024

    work page arXiv 2024

  8. [8]

    Training language models to follow instructions with human feedback,

    L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, et al., “Training language models to follow instructions with human feedback,”Advances in neural information processing systems, vol. 35, pp. 27 730–27 744, 2022

    work page 2022

  9. [9]

    A novel policy for pre-trained deep reinforcement learning for speech emotion recognition,

    T. Rajapakshe, R. Rana, S. Khalifa, J. Liu, and B. Schuller, “A novel policy for pre-trained deep reinforcement learning for speech emotion recognition,” inProceedings of the Australasian Computer Science Week, 2022, pp. 96–105

    work page 2022

  10. [10]

    A CRNN-GRU based rein- forcement learning approach to audio captioning.,

    X. Xu, H. Dinkel, M. Wu, and K. Yu, “A CRNN-GRU based rein- forcement learning approach to audio captioning.,” inProc. DCASE, 2020, pp. 225–229

    work page 2020

  11. [11]

    An encoder-decoder based audio captioning system with transfer and reinforcement learning for dcase challenge 2021 task 6,

    X. Mei, Q. Huang, X. Liu, G. Chen, J. Wu, Y . Wu, J. Zhao, S. Li, T. Ko, H. L. Tang, et al., “An encoder-decoder based audio captioning system with transfer and reinforcement learning for dcase challenge 2021 task 6,”DCASE2021 Challenge, Tech. Rep, Tech. Rep, 2021

    work page 2021

  12. [12]

    Beyond the status quo: A contemporary survey of advances and challenges in audio captioning,

    X. Xu, Z. Xie, M. Wu, and K. Yu, “Beyond the status quo: A contemporary survey of advances and challenges in audio captioning,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 32, pp. 95–112, 2023

    work page 2023

  13. [13]

    Audio self-supervised learning: A survey,

    S. Liu, A. Mallol-Ragolta, E. Parada-Cabaleiro, K. Qian, X. Jing, A. Kathan, B. Hu, and B. W. Schuller, “Audio self-supervised learning: A survey,”Patterns, vol. 3, no. 12, 2022

    work page 2022

  14. [14]

    Computer audition: From task-specific machine learning to foundation models,

    A. Triantafyllopoulos, I. Tsangko, A. Gebhard, A. Mesaros, T. Vir- tanen, and B. W. Schuller, “Computer audition: From task-specific machine learning to foundation models,”Proceedings of the IEEE, 2025

    work page 2025

  15. [15]

    SoundSpaces: Audio-Visual Navigation in 3D Environments,

    C. Chen, U. Jain, C. Schissler, S. V . A. Gari, Z. Al-Halah, V . K. Ithapu, P. Robinson, and K. Grauman, “SoundSpaces: Audio-Visual Navigation in 3D Environments,” inProc. ECCV, 2020

    work page 2020

  16. [16]

    Habitat: A platform for embodied ai research,

    M. Savva, A. Kadian, O. Maksymets, Y . Zhao, E. Wijmans, B. Jain, J. Straub, J. Liu, V . Koltun, J. Malik, et al., “Habitat: A platform for embodied ai research,” inProceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 9339–9347

    work page 2019

  17. [17]

    Move2hear: Active audio-visual source separation,

    S. Majumder, Z. Al-Halah, and K. Grauman, “Move2hear: Active audio-visual source separation,” inProc. ICCV, 2021, pp. 275–285

    work page 2021

  18. [18]

    Soundspaces 2.0: A simulation platform for visual-acoustic learning,

    C. Chen, C. Schissler, S. Garg, P. Kobernik, A. Clegg, P. Calamia, D. Batra, P. Robinson, and K. Grauman, “Soundspaces 2.0: A simulation platform for visual-acoustic learning,”Advances in Neural Information Processing Systems, vol. 35, pp. 8896–8911, 2022

    work page 2022

  19. [19]

    A unified audio-visual learning framework for localization, separation, and recognition,

    S. Mo and P. Morgado, “A unified audio-visual learning framework for localization, separation, and recognition,” inInternational Conference on Machine Learning, PMLR, 2023, pp. 25 006–25 017

    work page 2023

  20. [20]

    Younes, D

    A. Younes, D. Honerkamp, T. Welschehold, and A. Valada,Catch Me If You Hear Me: Audio-Visual Navigation in Complex Unmapped Environments with Moving Sounds, Jan. 2023. arXiv: 2111 . 14843 [cs]. Accessed: Aug. 21, 2025

    work page 2023

  21. [21]

    Agents that Listen: High- Throughput Reinforcement Learning with Multiple Sensory Systems,

    S. Hegde, A. Kanervisto, and A. Petrenko, “Agents that Listen: High- Throughput Reinforcement Learning with Multiple Sensory Systems,” in2021 IEEE Conference on Games (CoG), Copenhagen, Denmark: IEEE, Aug. 2021, pp. 1–5,ISBN: 978-1-6654-3886-5. Accessed: Aug. 21, 2025

    work page 2021

  22. [22]

    A Deep Reinforce- ment Learning Approach To Audio-Based Navigation In A Multi- Speaker Environment,

    P. Giannakopoulos, A. Pikrakis, and Y . Cotronis, “A Deep Reinforce- ment Learning Approach To Audio-Based Navigation In A Multi- Speaker Environment,” inICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada: IEEE, Jun. 2021, pp. 3475–3479,ISBN: 978-1- 7281-7605-5. Accessed: Aug. 21, 2025

    work page 2021

  23. [23]

    Development of the use of sound in the search behavior of infants.,

    A. E. Bigelow, “Development of the use of sound in the search behavior of infants.,”Developmental Psychology, vol. 19, no. 3, p. 317, 1983

    work page 1983

  24. [24]

    Reach on sound: A key to object permanence in visually impaired children,

    E. Fazzi, S. G. Signorini, M. Bomba, A. Luparia, J. Lanners, and U. Balottin, “Reach on sound: A key to object permanence in visually impaired children,”Early human development, vol. 87, no. 4, pp. 289– 296, 2011

    work page 2011

  25. [25]

    Sound effects: Multimodal input helps infants find dis- placed objects,

    J. L. Shinskey, “Sound effects: Multimodal input helps infants find dis- placed objects,”British Journal of Developmental Psychology, vol. 35, no. 3, pp. 317–333, 2017

    work page 2017

  26. [26]

    The development of blind infants’ search for dropped objects,

    A. Bigelow, “The development of blind infants’ search for dropped objects,”Infant Behavior and Development, vol. 7, p. 36, 1984

    work page 1984

  27. [27]

    Overview and evaluation of sound event localization and detection in dcase 2019,

    A. Politis, A. Mesaros, S. Adavanne, T. Heittola, and T. Virtanen, “Overview and evaluation of sound event localization and detection in dcase 2019,”IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 29, pp. 684–698, 2020

    work page 2019

  28. [28]

    Sound event detection: A tutorial,

    A. Mesaros, T. Heittola, T. Virtanen, and M. D. Plumbley, “Sound event detection: A tutorial,”IEEE Signal Processing Magazine, vol. 38, no. 5, pp. 67–83, 2021

    work page 2021

  29. [29]

    A theoretical analysis of deep q-learning,

    J. Fan, Z. Wang, Y . Xie, and Z. Yang, “A theoretical analysis of deep q-learning,” inLearning for dynamics and control, PMLR, 2020, pp. 486–489

    work page 2020

  30. [30]

    Self-improving reactive agents based on reinforcement learning, planning and teaching,

    L.-J. Lin, “Self-improving reactive agents based on reinforcement learning, planning and teaching,”Machine learning, vol. 8, no. 3, pp. 293–321, 1992

    work page 1992

  31. [31]

    Pyroomacoustics: A python package for audio room simulation and array processing algorithms,

    R. Scheibler, E. Bezzam, and I. Dokmani ´c, “Pyroomacoustics: A python package for audio room simulation and array processing algorithms,” inProc. ICASSP, IEEE, 2018, pp. 351–355

    work page 2018

  32. [32]

    gpuRIR: A python library for room impulse response simulation with GPU acceleration,

    D. Diaz-Guerra, A. Miguel, and J. R. Beltran, “gpuRIR: A python library for room impulse response simulation with GPU acceleration,” Multimedia Tools and Applications, vol. 80, no. 4, pp. 5653–5671, 2021

    work page 2021

  33. [33]

    Vizdoom: A doom-based AI research platform for visual reinforce- ment learning,

    M. Kempka, M. Wydmuch, G. Runc, J. Toczek, and W. Ja ´skowski, “Vizdoom: A doom-based AI research platform for visual reinforce- ment learning,” inProc. IEEE Conference on Computational Intelli- gence and Games (CIG), IEEE, 2016, pp. 1–8

    work page 2016

  34. [34]

    Acoustic volume rendering for neural impulse response fields,

    Z. Lan, C. Zheng, Z. Zheng, and M. Zhao, “Acoustic volume rendering for neural impulse response fields,”Advances in Neural Information Processing Systems, vol. 37, pp. 44 600–44 623, 2024

    work page 2024

  35. [35]

    Mesh2ir: Neural acoustic impulse response generator for complex 3d scenes,

    A. Ratnarajah, Z. Tang, R. Aralikatti, and D. Manocha, “Mesh2ir: Neural acoustic impulse response generator for complex 3d scenes,” inProc. ACM Multimedia, 2022, pp. 924–933

    work page 2022

  36. [36]

    Panns: Large-scale pretrained audio neural networks for audio pattern recognition,

    Q. Kong, Y . Cao, T. Iqbal, Y . Wang, W. Wang, and M. D. Plumb- ley, “Panns: Large-scale pretrained audio neural networks for audio pattern recognition,”IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 28, pp. 2880–2894, 2020

    work page 2020