{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T06:47:32Z","timestamp":1757314052447},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684048","type":"print"},{"value":"9781643684055","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,6,22]],"date-time":"2023-06-22T00:00:00Z","timestamp":1687392000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,22]]},"abstract":"<jats:p>The fast Continuous Wavelet Transform (fCWT) is used to improve Deep Convolutional Neural Networks (DCNN)\u2019s Speech Emotion Recognition (SER). While being computationally efficient, the fCWT\u2019s time-frequency analysis overcomes traditional methods\u2019 resolution limitations (e.g., Short-Term Fourier Transform). fCWT-induced DCNNs are compared to state-of-the-art DCNN SER systems. Comparing different wavelet parameters, we also provide an empirical strategy for balancing temporal and spectral features in speech signals. We suggest that this strategy is of generic interest for non-stationary signal processing where large amounts of data are available. fCWT\u2019s potential for improving SER accuracy in real-time applications is confirmed. In parallel, the variance in the cross-validation folds confirmed deep learning\u2019s vulnerability on non-big data sets.<\/jats:p>","DOI":"10.3233\/aise230012","type":"book-chapter","created":{"date-parts":[[2023,6,23]],"date-time":"2023-06-23T10:04:30Z","timestamp":1687514670000},"source":"Crossref","is-referenced-by-count":3,"title":["Speech Emotion Recognition Using Deep Convolutional Neural Networks Improved by the Fast Continuous Wavelet Transform"],"prefix":"10.3233","author":[{"given":"Bj\u00f6rn E.","family":"Van Zwol","sequence":"first","affiliation":[{"name":"Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mathijs A.","family":"Langezaal","sequence":"additional","affiliation":[{"name":"Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands"},{"name":"Population-Based Epidemiological Cohorts Unit UMS11, INSERM, Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lukas P.A.","family":"Arts","sequence":"additional","affiliation":[{"name":"Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Albert","family":"Gatt","sequence":"additional","affiliation":[{"name":"Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Egon L.","family":"Van Den Broek","sequence":"additional","affiliation":[{"name":"Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Ambient Intelligence and Smart Environments","Workshop Proceedings of the 19th International Conference on Intelligent Environments (IE2023)"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/AISE230012","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,23]],"date-time":"2023-06-23T22:52:09Z","timestamp":1687560729000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/AISE230012"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,22]]},"ISBN":["9781643684048","9781643684055"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/aise230012","relation":{},"ISSN":["1875-4163","1875-4171"],"issn-type":[{"value":"1875-4163","type":"print"},{"value":"1875-4171","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,22]]}}}