Audio and Acoustic Signal Processing

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Associated SPS Event: IEEE ICASSP 2021 Grand Challenge

The ICASSP 2021 Deep Noise Suppression (DNS) challenge is designed to foster innovation in the field of noise suppression to achieve superior perceptual speech quality. We recently organized a DNS challenge special session at INTERSPEECH 2020. We open sourced training and test datasets for researchers to train their noise suppression models. We also open sourced a subjective evaluation framework and used the tool to evaluate and pick the final winners. Many researchers from academia and industry made significant contributions to push the field forward.

Associated SPS Event: IEEE ICASSP 2021 Grand Challenge

Text-to-speech (TTS) or speech synthesis has witnessed significant performance improvement with the help of deep learning. The latest advances in end-to-end text-to-speech paradigm and neural vocoder have enabled us to produce very realistic and natural-sounding synthetic speech reaching almost human-parity performance. But this amazing ability is still limited to the ideal scenarios with a large single-speaker less-expressive training set.

Associated SPS Event: IEEE ICASSP 2021 Grand Challenge

The ICASSP 2021 Acoustic Echo Cancellation Challenge is intended to stimulate research in the area of acoustic echo cancellation (AEC), which is an important part of speech enhancement and still a top issue in audio communication and conferencing systems. Many recent AEC studies report good performance on synthetic datasets where the train and test samples come from the same underlying distribution.

Past Members

Past Members

The following lists all the past chairs and members of the SPS Audio and Acoustic Signal Processing Technical Committee. 

*NOTE: Please scroll up/down and left/right within the Past Members window to view the full list. Also, view the full downloadable list (right-click, Save As to save file).

The workshop aims to provide a venue for researchers working on computational analysis of sound events and scene analysis to present and discuss their results. We aim to bring together researchers from many different universities and companies with interest in the topic, and provide the opportunity for scientific exchange of ideas and opinions. 

The challenge will consider the problem of distant multi-microphone conversational speech recognition in everyday home environments. Speech material was elicited using a dinner party scenario with efforts taken to capture data that is representative of natural conversational speech.

Recently, substantial progress has been made in the field of reverberant speech signal processing, including both single- and multi-channel de-reverberation techniques, and automatic speech recognition (ASR) techniques robust to reverberation. REVERB (REverberant Voice Enhancement and Recognition Benchmark) challenge that provides an opportunity to the researchers in the field to carry out a comprehensive evaluation of their methods based on a common database and on common evaluation metrics.

The ACE Challenge was part of the programme of Challenges organised by the IEEE Audio and Acoustic Signal Processing Technical Committee. The aim of this challenge was to evaluate state-of-the-art algorithms for blind acoustic parameter estimation from speech and to promote the emerging area of research in this field. Participants will evaluate their algorithms for T60 and DRR estimation against the ‘ground truth’ values provided with the data-sets. Furthermore, they are expected to present the results in a paper describing the method used.

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