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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2503.18590">arXiv:2503.18590</a> <span> [<a href="https://arxiv.org/pdf/2503.18590">pdf</a>, <a href="https://arxiv.org/format/2503.18590">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Target Speaker Selection for Neural Network Beamforming in Multi-Speaker Scenarios </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Fiorio%2C+L+V">Luan Vin铆cius Fiorio</a>, <a href="/search/eess?searchtype=author&query=Defraene%2C+B">Bruno Defraene</a>, <a href="/search/eess?searchtype=author&query=David%2C+J">Johan David</a>, <a href="/search/eess?searchtype=author&query=Young%2C+A">Alex Young</a>, <a href="/search/eess?searchtype=author&query=Widdershoven%2C+F">Frans Widdershoven</a>, <a href="/search/eess?searchtype=author&query=van+Houtum%2C+W">Wim van Houtum</a>, <a href="/search/eess?searchtype=author&query=Aarts%2C+R+M">Ronald M. Aarts</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2503.18590v1-abstract-short" style="display: inline;"> We propose a speaker selection mechanism (SSM) for the training of an end-to-end beamforming neural network, based on recent findings that a listener usually looks to the target speaker with a certain undershot angle. The mechanism allows the neural network model to learn toward which speaker to focus, during training, in a multi-speaker scenario, based on the position of listener and speakers. Ho… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.18590v1-abstract-full').style.display = 'inline'; document.getElementById('2503.18590v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.18590v1-abstract-full" style="display: none;"> We propose a speaker selection mechanism (SSM) for the training of an end-to-end beamforming neural network, based on recent findings that a listener usually looks to the target speaker with a certain undershot angle. The mechanism allows the neural network model to learn toward which speaker to focus, during training, in a multi-speaker scenario, based on the position of listener and speakers. However, only audio information is necessary during inference. We perform acoustic simulations demonstrating the feasibility and performance when the SSM is employed in training. The results show significant increase in speech intelligibility, quality, and distortion metrics when compared to the minimum variance distortionless filter and the same neural network model trained without SSM. The success of the proposed method is a significant step forward toward the solution of the cocktail party problem. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.18590v1-abstract-full').style.display = 'none'; document.getElementById('2503.18590v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2503.18579">arXiv:2503.18579</a> <span> [<a href="https://arxiv.org/pdf/2503.18579">pdf</a>, <a href="https://arxiv.org/format/2503.18579">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Unsupervised Variational Acoustic Clustering </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Fiorio%2C+L+V">Luan Vin铆cius Fiorio</a>, <a href="/search/eess?searchtype=author&query=Defraene%2C+B">Bruno Defraene</a>, <a href="/search/eess?searchtype=author&query=David%2C+J">Johan David</a>, <a href="/search/eess?searchtype=author&query=Widdershoven%2C+F">Frans Widdershoven</a>, <a href="/search/eess?searchtype=author&query=van+Houtum%2C+W">Wim van Houtum</a>, <a href="/search/eess?searchtype=author&query=Aarts%2C+R+M">Ronald M. Aarts</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2503.18579v1-abstract-short" style="display: inline;"> We propose an unsupervised variational acoustic clustering model for clustering audio data in the time-frequency domain. The model leverages variational inference, extended to an autoencoder framework, with a Gaussian mixture model as a prior for the latent space. Specifically designed for audio applications, we introduce a convolutional-recurrent variational autoencoder optimized for efficient ti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.18579v1-abstract-full').style.display = 'inline'; document.getElementById('2503.18579v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.18579v1-abstract-full" style="display: none;"> We propose an unsupervised variational acoustic clustering model for clustering audio data in the time-frequency domain. The model leverages variational inference, extended to an autoencoder framework, with a Gaussian mixture model as a prior for the latent space. Specifically designed for audio applications, we introduce a convolutional-recurrent variational autoencoder optimized for efficient time-frequency processing. Our experimental results considering a spoken digits dataset demonstrate a significant improvement in accuracy and clustering performance compared to traditional methods, showcasing the model's enhanced ability to capture complex audio patterns. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.18579v1-abstract-full').style.display = 'none'; document.getElementById('2503.18579v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.15582">arXiv:2408.15582</a> <span> [<a href="https://arxiv.org/pdf/2408.15582">pdf</a>, <a href="https://arxiv.org/format/2408.15582">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> Spectral Masking with Explicit Time-Context Windowing for Neural Network-Based Monaural Speech Enhancement </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Fiorio%2C+L+V">Luan Vin铆cius Fiorio</a>, <a href="/search/eess?searchtype=author&query=Karanov%2C+B">Boris Karanov</a>, <a href="/search/eess?searchtype=author&query=Defraene%2C+B">Bruno Defraene</a>, <a href="/search/eess?searchtype=author&query=David%2C+J">Johan David</a>, <a href="/search/eess?searchtype=author&query=van+Houtum%2C+W">Wim van Houtum</a>, <a href="/search/eess?searchtype=author&query=Widdershoven%2C+F">Frans Widdershoven</a>, <a href="/search/eess?searchtype=author&query=Aarts%2C+R+M">Ronald M. Aarts</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.15582v1-abstract-short" style="display: inline;"> We propose and analyze the use of an explicit time-context window for neural network-based spectral masking speech enhancement to leverage signal context dependencies between neighboring frames. In particular, we concentrate on soft masking and loss computed on the time-frequency representation of the reconstructed speech. We show that the application of a time-context windowing function at both i… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.15582v1-abstract-full').style.display = 'inline'; document.getElementById('2408.15582v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.15582v1-abstract-full" style="display: none;"> We propose and analyze the use of an explicit time-context window for neural network-based spectral masking speech enhancement to leverage signal context dependencies between neighboring frames. In particular, we concentrate on soft masking and loss computed on the time-frequency representation of the reconstructed speech. We show that the application of a time-context windowing function at both input and output of the neural network model improves the soft mask estimation process by combining multiple estimates taken from different contexts. The proposed approach is only applied as post-optimization in inference mode, not requiring additional layers or special training for the neural network model. Our results show that the method consistently increases both intelligibility and signal quality of the denoised speech, as demonstrated for two classes of convolutional-based speech enhancement models. Importantly, the proposed method requires only a negligible ($\leq1\%$) increase in the number of model parameters, making it suitable for hardware-constrained applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.15582v1-abstract-full').style.display = 'none'; document.getElementById('2408.15582v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This work has been submitted to the IEEE for possible publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.02778">arXiv:2306.02778</a> <span> [<a href="https://arxiv.org/pdf/2306.02778">pdf</a>, <a href="https://arxiv.org/format/2306.02778">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> EffCRN: An Efficient Convolutional Recurrent Network for High-Performance Speech Enhancement </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Sach%2C+M">Marvin Sach</a>, <a href="/search/eess?searchtype=author&query=Franzen%2C+J">Jan Franzen</a>, <a href="/search/eess?searchtype=author&query=Defraene%2C+B">Bruno Defraene</a>, <a href="/search/eess?searchtype=author&query=Fluyt%2C+K">Kristoff Fluyt</a>, <a href="/search/eess?searchtype=author&query=Strake%2C+M">Maximilian Strake</a>, <a href="/search/eess?searchtype=author&query=Tirry%2C+W">Wouter Tirry</a>, <a href="/search/eess?searchtype=author&query=Fingscheidt%2C+T">Tim Fingscheidt</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.02778v1-abstract-short" style="display: inline;"> Fully convolutional recurrent neural networks (FCRNs) have shown state-of-the-art performance in single-channel speech enhancement. However, the number of parameters and the FLOPs/second of the original FCRN are restrictively high. A further important class of efficient networks is the CRUSE topology, serving as reference in our work. By applying a number of topological changes at once, we propose… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.02778v1-abstract-full').style.display = 'inline'; document.getElementById('2306.02778v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.02778v1-abstract-full" style="display: none;"> Fully convolutional recurrent neural networks (FCRNs) have shown state-of-the-art performance in single-channel speech enhancement. However, the number of parameters and the FLOPs/second of the original FCRN are restrictively high. A further important class of efficient networks is the CRUSE topology, serving as reference in our work. By applying a number of topological changes at once, we propose both an efficient FCRN (FCRN15), and a new family of efficient convolutional recurrent neural networks (EffCRN23, EffCRN23lite). We show that our FCRN15 (875K parameters) and EffCRN23lite (396K) outperform the already efficient CRUSE5 (85M) and CRUSE4 (7.2M) networks, respectively, w.r.t. PESQ, DNSMOS and DeltaSNR, while requiring about 94% less parameters and about 20% less #FLOPs/frame. Thereby, according to these metrics, the FCRN/EffCRN class of networks provides new best-in-class network topologies for speech enhancement. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.02778v1-abstract-full').style.display = 'none'; document.getElementById('2306.02778v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages, 5 figures, accepted for Interspeech 2023</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 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