DasFormer: Deep Alternating Spectrogram Transformer for Multi/Single-Channel Speech Separation Article Swipe
Shuo Wang
,
Xiang‐Yu Kong
,
Xiulian Peng
,
Hesam Movassagh
,
Prakash Vinod
,
Yan Lü
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2302.10657
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2302.10657
For the task of speech separation, previous study usually treats multi-channel and single-channel scenarios as two research tracks with specialized solutions developed respectively. Instead, we propose a simple and unified architecture - DasFormer (Deep alternating spectrogram transFormer) to handle both of them in the challenging reverberant environments. Unlike frame-wise sequence modeling, each TF-bin in the spectrogram is assigned with an embedding encoding spectral and spatial information. With such input, DasFormer is then formed by multiple repetition of simple blocks each of which integrates 1) two multi-head self-attention (MHSA) modules alternately processing within each frequency bin & temporal frame of the spectrogram 2) MBConv before each MHSA for modeling local features on the spectrogram. Experiments show that DasFormer has a powerful ability to model the time-frequency representation, whose performance far exceeds the current SOTA models in multi-channel speech separation, and also achieves single-channel SOTA in the more challenging yet realistic reverberation scenario.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2302.10657
- https://arxiv.org/pdf/2302.10657
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4321594004
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4321594004Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2302.10657Digital Object Identifier
- Title
-
DasFormer: Deep Alternating Spectrogram Transformer for Multi/Single-Channel Speech SeparationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-02-21Full publication date if available
- Authors
-
Shuo Wang, Xiang‐Yu Kong, Xiulian Peng, Hesam Movassagh, Prakash Vinod, Yan LüList of authors in order
- Landing page
-
https://arxiv.org/abs/2302.10657Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2302.10657Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2302.10657Direct OA link when available
- Concepts
-
Spectrogram, Computer science, Speech recognition, Transformer, Bin, Channel (broadcasting), Source separation, Embedding, Reverberation, Artificial intelligence, Algorithm, Acoustics, Engineering, Telecommunications, Physics, Electrical engineering, VoltageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.research | 16 |
| abstract_inverted_index.sequence | 49 |
| abstract_inverted_index.spectral | 62 |
| abstract_inverted_index.temporal | 96 |
| abstract_inverted_index.DasFormer | 32, 69, 116 |
| abstract_inverted_index.developed | 21 |
| abstract_inverted_index.embedding | 60 |
| abstract_inverted_index.frequency | 93 |
| abstract_inverted_index.modeling, | 50 |
| abstract_inverted_index.realistic | 148 |
| abstract_inverted_index.scenario. | 150 |
| abstract_inverted_index.scenarios | 13 |
| abstract_inverted_index.solutions | 20 |
| abstract_inverted_index.frame-wise | 48 |
| abstract_inverted_index.integrates | 82 |
| abstract_inverted_index.multi-head | 85 |
| abstract_inverted_index.processing | 90 |
| abstract_inverted_index.repetition | 75 |
| abstract_inverted_index.Experiments | 113 |
| abstract_inverted_index.alternately | 89 |
| abstract_inverted_index.alternating | 34 |
| abstract_inverted_index.challenging | 44, 146 |
| abstract_inverted_index.performance | 127 |
| abstract_inverted_index.reverberant | 45 |
| abstract_inverted_index.separation, | 5, 137 |
| abstract_inverted_index.specialized | 19 |
| abstract_inverted_index.spectrogram | 35, 55, 100 |
| abstract_inverted_index.architecture | 30 |
| abstract_inverted_index.information. | 65 |
| abstract_inverted_index.spectrogram. | 112 |
| abstract_inverted_index.transFormer) | 36 |
| abstract_inverted_index.environments. | 46 |
| abstract_inverted_index.multi-channel | 10, 135 |
| abstract_inverted_index.respectively. | 22 |
| abstract_inverted_index.reverberation | 149 |
| abstract_inverted_index.self-attention | 86 |
| abstract_inverted_index.single-channel | 12, 141 |
| abstract_inverted_index.time-frequency | 124 |
| abstract_inverted_index.representation, | 125 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |