2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)最新文献_第5页

Ambient-Aware Sound Field Translation Using Optimal Spatial Filtering 使用最佳空间滤波的环境感知声场转换

2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) Pub Date : 2021-10-17 DOI: 10.1109/WASPAA52581.2021.9632793

Maximilian Kentgens, P. Jax

引用次数: 2

Fast Convergent Method for Active Noise Control Over Spatial Region with Causal Constraint 具有因果约束的空间区域主动噪声控制的快速收敛方法

2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) Pub Date : 2021-10-17 DOI: 10.1109/WASPAA52581.2021.9632744

Naoki Murata, Yuhta Takida, T. Magariyachi

引用次数: 0

Kernel Learning for Sound Field Estimation with L1 and L2 Regularizations 基于L1和L2正则化的声场估计核学习

2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) Pub Date : 2021-10-11 DOI: 10.1109/WASPAA52581.2021.9632731

Ryosuke Horiuchi, Shoichi Koyama, Juliano G. C. Ribeiro, Natsuki Ueno, H. Saruwatari

引用次数: 8

Auto-DSP: Learning to Optimize Acoustic Echo Cancellers 自动dsp:学习优化声学回声消除器

2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) Pub Date : 2021-10-08 DOI: 10.1109/WASPAA52581.2021.9632678

Jonah Casebeer, Nicholas J. Bryan, P. Smaragdis

{"title":"Auto-DSP: Learning to Optimize Acoustic Echo Cancellers","authors":"Jonah Casebeer, Nicholas J. Bryan, P. Smaragdis","doi":"10.1109/WASPAA52581.2021.9632678","DOIUrl":"https://doi.org/10.1109/WASPAA52581.2021.9632678","url":null,"abstract":"Adaptive filtering algorithms are commonplace in signal processing and have wide-ranging applications from single-channel denoising to multi -channel acoustic echo cancellation and adaptive beamforming. Such algorithms typically operate via specialized online, iterative optimization methods and have achieved tremendous success, but require expert knowledge, are slow to develop, and are difficult to customize. In our work, we present a new method to automatically learn adaptive filtering update rules directly from data. To do so, we frame adaptive filtering as a differentiable operator and train a learned optimizer to output a gradient descent-based update rule from data via backpropagation through time. We demonstrate our general approach on an acoustic echo cancellation task (single-talk with noise) and show that we can learn high-performing adaptive filters for a variety of common linear and non-linear mul-tidelayed block frequency domain filter architectures. We also find that our learned update rules exhibit fast convergence, can optimize in the presence of nonlinearities, and are robust to acoustic scene changes despite never encountering any during training.","PeriodicalId":429900,"journal":{"name":"2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122708780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

A Universal Deep Room Acoustics Estimator 通用深室声学估计器

2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) Pub Date : 2021-09-29 DOI: 10.1109/WASPAA52581.2021.9632738

P. S. López, Paul Callens, M. Cernak

引用次数: 8

Cross-Domain Semi-Supervised Audio Event Classification Using Contrastive Regularization 基于对比正则化的跨域半监督音频事件分类

2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) Pub Date : 2021-09-29 DOI: 10.1109/WASPAA52581.2021.9632721

Donmoon Lee, Kyogu Lee

引用次数: 2

Convolutive Prediction for Reverberant Speech Separation 混响语音分离的卷积预测

2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) Pub Date : 2021-08-16 DOI: 10.1109/WASPAA52581.2021.9632667

Zhong-Qiu Wang, G. Wichern, Jonathan Le Roux

引用次数: 5

A Streamwise Gan Vocoder for Wideband Speech Coding at Very Low Bit Rate 一种用于极低比特率下宽带语音编码的流式Gan声码器

2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) Pub Date : 2021-08-09 DOI: 10.1109/WASPAA52581.2021.9632750

Ahmed Mustafa, Jan Büthe, Srikanth Korse, Kishan Gupta, Guillaume Fuchs, N. Pia

{"title":"A Streamwise Gan Vocoder for Wideband Speech Coding at Very Low Bit Rate","authors":"Ahmed Mustafa, Jan Büthe, Srikanth Korse, Kishan Gupta, Guillaume Fuchs, N. Pia","doi":"10.1109/WASPAA52581.2021.9632750","DOIUrl":"https://doi.org/10.1109/WASPAA52581.2021.9632750","url":null,"abstract":"Recently, GAN vocoders have seen rapid progress in speech synthesis, starting to outperform autoregressive models in perceptual quality with much higher generation speed. However, autoregressive vocoders are still the common choice for neural generation of speech signals coded at very low bit rates. In this paper, we present a GAN vocoder which is able to generate wideband speech waveforms from parameters coded at 1.6 kbit/s. The proposed model is a modified version of the StyleMelGAN vocoder that can run in frame-by-frame manner, making it suitable for streaming applications. The experimental results show that the proposed model significantly outperforms prior autoregressive vocoders like LPC-Net for very low bit rate speech coding, with computational complexity of about 5 GMACs, providing a new state of the art in this domain. Moreover, this streamwise adversarial vocoder delivers quality competitive to advanced speech codecs such as EVS at 5.9 kbit/s on clean speech, which motivates further usage of feedforward fully-convolutional models for low bit rate speech coding.","PeriodicalId":429900,"journal":{"name":"2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115059716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

A Multi-Head Relevance Weighting Framework for Learning Raw Waveform Audio Representations 一种学习原始波形音频表示的多头相关加权框架

2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) Pub Date : 2021-07-30 DOI: 10.1109/WASPAA52581.2021.9632708

Debottam Dutta, Purvi Agrawal, Sriram Ganapathy

{"title":"A Multi-Head Relevance Weighting Framework for Learning Raw Waveform Audio Representations","authors":"Debottam Dutta, Purvi Agrawal, Sriram Ganapathy","doi":"10.1109/WASPAA52581.2021.9632708","DOIUrl":"https://doi.org/10.1109/WASPAA52581.2021.9632708","url":null,"abstract":"In this work, we propose a multi-head relevance weighting framework to learn audio representations from raw waveforms. The audio waveform, split into windows of short-duration, are processed with a 1-D convolutional layer of cosine modulated Gaussian filters acting as a learnable filterbank. The key novelty of the proposed framework is the introduction of multi-head relevance on the learnt filterbank representations. Each head of the relevance network is modelled as a separate sub-network. These heads perform representation enhancement by generating weight masks for different parts of the time-frequency representation learnt by the parametric acoustic filterbank layer. The relevance weighted representations are fed to a neural classifier and the whole system is trained jointly for the audio classification objective. Experiments are performed on the DCASE2020 Task 1A challenge as well as the Urban Sound Classification (USC) tasks. In these experiments, the proposed approach yields relative improvements of 10% and 23% respectively for the DCASE2020 and USC datasets over the mel-spectrogram baseline. Also, the analysis of multi-head relevance weights provides insights on the learned representations.","PeriodicalId":429900,"journal":{"name":"2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116957189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Blind Room Parameter Estimation Using Multiple Multichannel Speech Recordings 基于多通道语音记录的盲室参数估计

2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) Pub Date : 2021-07-29 DOI: 10.1109/WASPAA52581.2021.9632778

Prerak Srivastava, Antoine Deleforge, E. Vincent

引用次数: 6