{"title":"TF-CrossNet:利用全局、跨带、窄带和位置编码实现单声道和多声道扬声器分离","authors":"Vahid Ahmadi Kalkhorani;DeLiang Wang","doi":"10.1109/TASLP.2024.3492803","DOIUrl":null,"url":null,"abstract":"We introduce TF-CrossNet, a complex spectral mapping approach to speaker separation and enhancement in reverberant and noisy conditions. The proposed architecture comprises an encoder layer, a global multi-head self-attention module, a cross-band module, a narrow-band module, and an output layer. TF-CrossNet captures global, cross-band, and narrow-band correlations in the time-frequency domain. To address performance degradation in long utterances, we introduce a random chunk positional encoding. Experimental results on multiple datasets demonstrate the effectiveness and robustness of TF-CrossNet, achieving state-of-the-art performance in tasks including reverberant and noisy-reverberant speaker separation. Furthermore, TF-CrossNet exhibits faster and more stable training in comparison to recent baselines. Additionally, TF-CrossNet's high performance extends to multi-microphone conditions, demonstrating its versatility in various acoustic scenarios.","PeriodicalId":13332,"journal":{"name":"IEEE/ACM Transactions on Audio, Speech, and Language Processing","volume":"32 ","pages":"4999-5009"},"PeriodicalIF":4.1000,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"TF-CrossNet: Leveraging Global, Cross-Band, Narrow-Band, and Positional Encoding for Single- and Multi-Channel Speaker Separation\",\"authors\":\"Vahid Ahmadi Kalkhorani;DeLiang Wang\",\"doi\":\"10.1109/TASLP.2024.3492803\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We introduce TF-CrossNet, a complex spectral mapping approach to speaker separation and enhancement in reverberant and noisy conditions. The proposed architecture comprises an encoder layer, a global multi-head self-attention module, a cross-band module, a narrow-band module, and an output layer. TF-CrossNet captures global, cross-band, and narrow-band correlations in the time-frequency domain. To address performance degradation in long utterances, we introduce a random chunk positional encoding. Experimental results on multiple datasets demonstrate the effectiveness and robustness of TF-CrossNet, achieving state-of-the-art performance in tasks including reverberant and noisy-reverberant speaker separation. Furthermore, TF-CrossNet exhibits faster and more stable training in comparison to recent baselines. Additionally, TF-CrossNet's high performance extends to multi-microphone conditions, demonstrating its versatility in various acoustic scenarios.\",\"PeriodicalId\":13332,\"journal\":{\"name\":\"IEEE/ACM Transactions on Audio, Speech, and Language Processing\",\"volume\":\"32 \",\"pages\":\"4999-5009\"},\"PeriodicalIF\":4.1000,\"publicationDate\":\"2024-11-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE/ACM Transactions on Audio, Speech, and Language Processing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10745728/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ACOUSTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE/ACM Transactions on Audio, Speech, and Language Processing","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10745728/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ACOUSTICS","Score":null,"Total":0}
TF-CrossNet: Leveraging Global, Cross-Band, Narrow-Band, and Positional Encoding for Single- and Multi-Channel Speaker Separation
We introduce TF-CrossNet, a complex spectral mapping approach to speaker separation and enhancement in reverberant and noisy conditions. The proposed architecture comprises an encoder layer, a global multi-head self-attention module, a cross-band module, a narrow-band module, and an output layer. TF-CrossNet captures global, cross-band, and narrow-band correlations in the time-frequency domain. To address performance degradation in long utterances, we introduce a random chunk positional encoding. Experimental results on multiple datasets demonstrate the effectiveness and robustness of TF-CrossNet, achieving state-of-the-art performance in tasks including reverberant and noisy-reverberant speaker separation. Furthermore, TF-CrossNet exhibits faster and more stable training in comparison to recent baselines. Additionally, TF-CrossNet's high performance extends to multi-microphone conditions, demonstrating its versatility in various acoustic scenarios.
期刊介绍:
The IEEE/ACM Transactions on Audio, Speech, and Language Processing covers audio, speech and language processing and the sciences that support them. In audio processing: transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. In speech processing: areas such as speech analysis, synthesis, coding, speech and speaker recognition, speech production and perception, and speech enhancement. In language processing: speech and text analysis, understanding, generation, dialog management, translation, summarization, question answering and document indexing and retrieval, as well as general language modeling.