19 research outputs found
Efficient Multichannel Nonlinear Acoustic Echo Cancellation Based on a Cooperative Strategy
No full text
ConcateNet: Dialogue Separation Using Local And Global Feature Concatenation
No full textDialogue separation involves isolating a dialogue signal from a mixture, such as a movie or a TV program. This can be a necessary step to enable dialogue enhancement for broadcast-related applications. In this paper, ConcateNet for dialogue separation is proposed, which is based on a novel approach for processing local and global features aimed at better generalization for out-of-domain signals. ConcateNet is trained using a noise reduction-focused, publicly available dataset and evaluated using three datasets: two noise reduction-focused datasets (in-domain), which show competitive performance for ConcateNet, and a broadcast-focused dataset (out-of-domain), which verifies the better generalization performance for the proposed architecture compared to considered state-of-the-art noise-reduction methods
Exploiting spatial information with the informed complex-valued spatial autoencoder for target speaker extraction
No full textIn conventional multichannel audio signal enhancement, spatial and spectral
filtering are often performed sequentially. In contrast, it has been shown that
for neural spatial filtering a joint approach of spectro-spatial filtering is
more beneficial. In this contribution, we investigate the spatial filtering
performed by such a time-varying spectro-spatial filter. We extend the recently
proposed complex-valued spatial autoencoder (COSPA) for the task of target
speaker extraction by leveraging its interpretable structure and purposefully
informing the network of the target speaker's position. We show that the
resulting informed COSPA (iCOSPA) effectively and flexibly extracts a target
speaker from a mixture of speakers. We also find that the proposed architecture
is well capable of learning pronounced spatial selectivity patterns and show
that the results depend significantly on the training target and the reference
signal when computing various evaluation metrics.Comment: Accepted to 2023 IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP), Rhodes Island, Greece. 5 pages, 2 figure
Navigating PESQ: Up-to-Date Versions and Open Implementations
No full text5357Perceptual Evaluation of Speech Quality (PESQ) is an objective quality measure that remains widely used despite its withdrawal by the International Telecommunication Union (ITU). PESQ has evolved over two decades, with multiple versions and publicly available implementations emerging during this time. Different versions and their updates can be overwhelming, especially for new PESQ users. This work provides practical guidance on the different versions and implementations of PESQ. We show that differences can be significant, especially between PESQ versions. We stress the importance of specifying the exact version and implementation that is used to compute PESQ, and possibly to detail how multi-channel signals are handled. These practices would facilitate the interpretation of results and allow comparisons of PESQ scores between different studies. We also provide a repository that implements the latest corrections to PESQ, i.e., Corrigendum 2, which is not implemented by any other openly available distribution: https://github.com/audiolabs/PESQ
Evolutionary Resampling for Multi-Target Tracking using Probability Hypothesis Density Filter
No full textRobust Speech Activity Detection in the Presence of Singing Voice
No full textSpeech Activity Detection (SAD) systems often misclassify singing as speech, leading to degraded performance in applications such as dialogue enhancement and automatic speech recognition. We introduce Singing-Robust Speech Activity Detection (SR-SAD), a neural network designed to robustly detect speech in the presence of singing. Our key contributions are: i) a training strategy using controlled ratios of speech and singing samples to improve discrimination, ii) a computationally efficient model that maintains robust performance while reducing inference runtime, and iii) a new evaluation metric tailored to assess SAD robustness in mixed speech-singing scenarios. Experiments on a challenging dataset spanning multiple musical genres show that SR-SAD maintains high speech detection accuracy (AUC = 0.919) while rejecting singing. By explicitly learning to distinguish between speech and singing, SR-SAD enables more reliable SAD in mixed speech-singing scenarios
ConcateNet: Dialogue Separation Using Local and Global Feature Concatenation
No full text5054Dialogue separation involves isolating a dialogue signal from a mixture, such as a movie or a TV program. This can be a necessary step to enable dialogue enhancement for broadcast-related applications. In this paper, ConcateNet for dialogue separation is proposed, which is based on a novel approach for processing local and global features aimed at better generalization for out-of-domain signals. ConcateNet is trained using a noise reduction-focused, publicly available dataset and evaluated using three datasets: two noise reduction-focused datasets (in-domain), which show competitive performance for ConcateNet, and a broadcast-focused dataset (out-of-domain), which verifies the better generalization performance for the proposed architecture compared to considered state-of-the-art noise-reduction methods
