{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/monaural-speech-enhancement-with-complex","title":"Monaural Speech Enhancement with Complex Convolutional Block Attention Module and Joint Time Frequency Losses","arxiv_id":"2102.01993","date":"2021-02-03","proceeding":null,"authors":["Shengkui Zhao","Trung Hieu Nguyen","Bin Ma"],"abstract":"Deep complex U-Net structure and convolutional recurrent network (CRN) structure achieve state-of-the-art performance for monaural speech enhancement. Both deep complex U-Net and CRN are encoder and decoder structures with skip connections, which heavily rely on the representation power of the complex-valued convolutional layers. In this paper, we propose a complex convolutional block attention module (CCBAM) to boost the representation power of the complex-valued convolutional layers by constructing more informative features. The CCBAM is a lightweight and general module which can be easily integrated into any complex-valued convolutional layers. We integrate CCBAM with the deep complex U-Net and CRN to enhance their performance for speech enhancement. We further propose a mixed loss function to jointly optimize the complex models in both time-frequency (TF) domain and time domain. By integrating CCBAM and the mixed loss, we form a new end-to-end (E2E) complex speech enhancement framework. Ablation experiments and objective evaluations show the superior performance of the proposed approaches (https://github.com/modelscope/ClearerVoice-Studio).","url_abs":"https://arxiv.org/abs/2102.01993v2","url_pdf":"https://arxiv.org/pdf/2102.01993v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"monaural-speech-enhancement-with-complex","repo_url":"https://github.com/modelscope/ClearerVoice-Studio","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"monaural-speech-enhancement-with-complex","repo_url":"https://github.com/alibabasglab/frcrn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"speech-denoising","task_name":"Speech Denoising"},{"task_slug":"speech-enhancement","task_name":"Speech Enhancement"}],"methods":[{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"crn","method_name":"CRN"},{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/speech-enhancement-on-interspeech-2020-deep","task":"Speech Enhancement","dataset":"DNS Challenge","model":"DCCRN-MC","rank_in_archive_order":2,"of":5,"metrics":{"PESQ-NB":"3.21"},"uses_additional_data":false},{"leaderboard":"/sota/speech-enhancement-on-interspeech-2020-deep","task":"Speech Enhancement","dataset":"DNS Challenge","model":"DCCRN-M","rank_in_archive_order":3,"of":5,"metrics":{"PESQ-NB":"3.15"},"uses_additional_data":false},{"leaderboard":"/sota/speech-enhancement-on-interspeech-2020-deep","task":"Speech Enhancement","dataset":"DNS Challenge","model":"DCCRN","rank_in_archive_order":4,"of":5,"metrics":{"PESQ-NB":"3.04"},"uses_additional_data":false},{"leaderboard":"/sota/speech-enhancement-on-deep-noise-suppression","task":"Speech Enhancement","dataset":"Deep Noise Suppression (DNS) Challenge","model":"FRCRN","rank_in_archive_order":13,"of":36,"metrics":{"PESQ-WB":"3.23"},"uses_additional_data":false},{"leaderboard":"/sota/speech-enhancement-on-demand","task":"Speech Enhancement","dataset":"VoiceBank + DEMAND","model":"D2Former","rank_in_archive_order":14,"of":42,"metrics":{"PESQ (wb)":"3.43","Para. (M)":"0.86"},"uses_additional_data":false},{"leaderboard":"/sota/speech-enhancement-on-wsj0-demand-rnnoise","task":"Speech Enhancement","dataset":"WSJ0 + DEMAND + RNNoise","model":"DCUNet-MC","rank_in_archive_order":1,"of":3,"metrics":{"PESQ-NB":"3.44"},"uses_additional_data":false},{"leaderboard":"/sota/speech-enhancement-on-wsj0-demand-rnnoise","task":"Speech Enhancement","dataset":"WSJ0 + DEMAND + RNNoise","model":"DCCRN-M","rank_in_archive_order":2,"of":3,"metrics":{"PESQ-NB":"3.28"},"uses_additional_data":false},{"leaderboard":"/sota/speech-enhancement-on-wsj0-demand-rnnoise","task":"Speech Enhancement","dataset":"WSJ0 + DEMAND + RNNoise","model":"DCUNet","rank_in_archive_order":3,"of":3,"metrics":{"PESQ-NB":"3.25"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2102.01993","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}