Pith Number

pith:CXHPACCV

pith:2026:CXHPACCVTKPXR53WQ4DKEBFKEH

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Flexible Multi-Channel Target Speaker Extraction Using Geometry-Conditioned Spatially Selective Non-linear Filters

Jiatong Li, Simon Doclo, Wiebke Middelberg

Geometry conditioning lets a spatially selective filter generalize target speaker extraction across different microphone array shapes.

arxiv:2605.18442 v1 · 2026-05-18 · eess.AS

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Claims

C1strongest claim

The proposed GC-SSF generalizes better to mismatched geometries while maintaining high spatial selectivity, as demonstrated by experimental results across circular, uniform linear, and random microphone arrays.

C2weakest assumption

That the geometry-conditioning branch using FiLM layers and the DOA-MPE feature can effectively capture and apply the spatial relationship between microphone positions and target speaker direction to adapt the SSF filtering process.

C3one line summary

GC-SSF with DOA-MPE feature generalizes target speaker extraction to mismatched microphone array geometries while preserving spatial selectivity.

References

33 extracted · 33 resolved · 1 Pith anchors

[1] INTRODUCTION Extracting a target speaker from a mixture of speakers and background noise remains a fundamental challenge in acoustic signal processing [1]. To discriminate the target speaker from the

[2] Flexible Multi-Channel Target Speaker Extraction Using Geometry-Conditioned Spatially Selective Non-linear Filters 2026 · arXiv:2605.18442

[3] SP A TIALL Y SELECTIVE NON-LINEAR FIL TER In this section, we review the spatially selective non-linear filter (SSF) for target speaker extraction [8], which serves as the baseline system. In the shor

[4] PROPOSED GEOMETRY -CONDITIONED SP A TIALL Y SELECTIVE NON-LINEAR FIL TER To improve the generalization ability of the SSF system across different microphone array geometries for a fixed number of micr

[5] EXPERIMENTS This section first presents the experimental setup, including the training and evaluation datasets, the network structure, and the training procedure. Then, the experimental results are pr

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Flexible Multi-Channel Target Speaker Extraction Using Geometry-Conditioned Spatially Selective Non-linear Filters

Receipt and verification

First computed	2026-05-20T00:06:01.262897Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

15cef008559a9f78f7768706a204aa21ffeccfdfa967bfa1bb287999da6e826a

Aliases

arxiv: 2605.18442 · arxiv_version: 2605.18442v1 · doi: 10.48550/arxiv.2605.18442 · pith_short_12: CXHPACCVTKPX · pith_short_16: CXHPACCVTKPXR53W · pith_short_8: CXHPACCV

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/CXHPACCVTKPXR53WQ4DKEBFKEH \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 15cef008559a9f78f7768706a204aa21ffeccfdfa967bfa1bb287999da6e826a

Canonical record JSON

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    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "primary_cat": "eess.AS",
    "submitted_at": "2026-05-18T14:11:37Z",
    "title_canon_sha256": "2c5e269f13d915e84f5dc92d5ea52725f20022db2b7bdda0d7660ce39af687ef"
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