{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T06:06:50Z","timestamp":1790748410526,"version":"4.1.0"},"reference-count":70,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,23]],"date-time":"2022-02-23T00:00:00Z","timestamp":1645574400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR-19-CE38-0001- 694 01"],"award-info":[{"award-number":["ANR-19-CE38-0001- 694 01"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]},{"name":"GENCI-IDRIS","award":["2021- 695 AD011011177R1"],"award-info":[{"award-number":["2021- 695 AD011011177R1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The use of the mel spectrogram as a signal parameterization for voice generation is quite recent and linked to the development of neural vocoders. These are deep neural networks that allow reconstructing high-quality speech from a given mel spectrogram. While initially developed for speech synthesis, now neural vocoders have also been studied in the context of voice attribute manipulation, opening new means for voice processing in audio production. However, to be able to apply neural vocoders in real-world applications, two problems need to be addressed: (1) To support use in professional audio workstations, the computational complexity should be small, (2) the vocoder needs to support a large variety of speakers, differences in voice qualities, and a wide range of intensities potentially encountered during audio production. In this context, the present study will provide a detailed description of the Multi-band Excited WaveNet, a fully convolutional neural vocoder built around signal processing blocks. It will evaluate the performance of the vocoder when trained on a variety of multi-speaker and multi-singer databases, including an experimental evaluation of the neural vocoder trained on speech and singing voices. Addressing the problem of intensity variation, the study will introduce a new adaptive signal normalization scheme that allows for robust compensation for dynamic and static gain variations. Evaluations are performed using objective measures and a number of perceptual tests including different neural vocoder algorithms known from the literature. The results confirm that the proposed vocoder compares favorably to the state-of-the-art in its capacity to generalize to unseen voices and voice qualities. The remaining challenges will be discussed.<\/jats:p>","DOI":"10.3390\/info13030103","type":"journal-article","created":{"date-parts":[[2022,2,23]],"date-time":"2022-02-23T22:54:02Z","timestamp":1645656842000},"page":"103","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Neural Vocoding for Singing and Speaking Voices with the Multi-Band Excited WaveNet"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6136-4391","authenticated-orcid":false,"given":"Axel","family":"Roebel","sequence":"first","affiliation":[{"name":"Analysis\/Synthesis Team\u2014UMR 9912 STMS, IRCAM (the Institute for Research and Coordination in Acoustics\/Music), CNRS (Centre National de la Recherche Scientifique), Sorbonne Universit\u00e9, 75004 Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7477-7600","authenticated-orcid":false,"given":"Frederik","family":"Bous","sequence":"additional","affiliation":[{"name":"Analysis\/Synthesis Team\u2014UMR 9912 STMS, IRCAM (the Institute for Research and Coordination in Acoustics\/Music), CNRS (Centre National de la Recherche Scientifique), Sorbonne Universit\u00e9, 75004 Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1121\/1.1916020","article-title":"Remaking Speech","volume":"11","author":"Dudley","year":"1939","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.1002\/j.1538-7305.1966.tb01706.x","article-title":"Phase Vocoder","volume":"45","author":"Flanagan","year":"1966","journal-title":"Bell Syst. Tech. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1109\/TASSP.1986.1164910","article-title":"Speech Analysis-Synthesis Based on a Sinusoidal Representation","volume":"34","author":"McAulay","year":"1986","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/0167-6393(90)90021-Z","article-title":"Pitch-Synchronous Waveform Processing Techniques for Text-to-Speech Synthesis Using Diphones","volume":"9","author":"Moulines","year":"1990","journal-title":"Speech Commun."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1109\/78.120793","article-title":"Shape Invariant Time-Scale and Pitch Modification of Speech","volume":"40","author":"Quatieri","year":"1992","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/S0167-6393(98)00085-5","article-title":"Restructuring Speech Representations Using a Pitch-Adaptive Time-Frequency Smoothing and an Instantaneous-Frequency-Based F0extraction: Possible Role of a Repetitive Structure in Sounds","volume":"27","author":"Kawahara","year":"1999","journal-title":"Speech Commun."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kawahara, H., Morise, M., Takahashi, T., Nisimura, R., Irino, T., and Banno, H. (April, January 30). TANDEM-STRAIGHT: A temporally stable power spectral representation for periodic signals and applications to interference-free spectrum, F0, and aperiodicity estimation. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2008), Las Vegas, USA.","DOI":"10.1109\/ICASSP.2008.4518514"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1016\/j.specom.2009.04.004","article-title":"Statistical Parametric Speech Synthesis","volume":"51","author":"Zen","year":"2009","journal-title":"Speech Commun."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"R\u00f6bel, A. (2010, January 26\u201330). Shape-Invariant Speech Transformation with the Phase Vocoder. Proceedings of the International Conference on Spoken Language Processing (INTERSPEECH 2010), Makuhari, Japan.","DOI":"10.21437\/Interspeech.2010-592"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.specom.2012.08.010","article-title":"Mixed Source Model and Its Adapted Vocal-Tract Filter Estimate for Voice Transformation and Synthesis","volume":"55","author":"Degottex","year":"2013","journal-title":"Speech Commun."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1877","DOI":"10.1587\/transinf.2015EDP7457","article-title":"WORLD: A Vocoder-Based High-Quality Speech Synthesis System for Real-Time Applications","volume":"99","author":"Morise","year":"2016","journal-title":"IEICE Trans Inf. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Markel, J.D., and Gray, A.H. (1976). Linear Prediction of Speech, Springer.","DOI":"10.1007\/978-3-642-66286-7"},{"key":"ref_13","first-page":"21","article-title":"The Source Filter Concept in Voice Production","volume":"22","author":"Fant","year":"1981","journal-title":"STL-QPSR Dept Speech Music Hear. KTH"},{"key":"ref_14","unstructured":"Stevens, K.N., and Hirano, M. (1981). Acoustic interaction between the glottal source and the vocal tract. Vocal Fold Physiology, University of Tokyo Press."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1902","DOI":"10.1121\/1.2832339","article-title":"Nonlinear Source\u2013Filter Coupling in Phonation: Theory","volume":"123","author":"Titze","year":"2008","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Shen, J., Pang, R., Weiss, R.J., Schuster, M., Jaitly, N., Yang, Z., Chen, Z., Zhang, Y., Wang, Y., and Skerrv-Ryan, R. (2018, January 15\u201320). Natural TTS synthesis by conditioning wavenet on MEL spectrogram predictions. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2018), Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8461368"},{"key":"ref_17","unstructured":"van den Oord, A., Li, Y., Babuschkin, I., Simonyan, K., Vinyals, O., Kavukcuoglu, K., van den Driessche, G., Lockhart, E., Cobo, L.C., and Stimberg, F. (2017). Parallel WaveNet: Fast High-Fidelity Speech Synthesis. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Prenger, R., Valle, R., and Catanzaro, B. (2019, January 12\u201317). Waveglow: A flow-based generative network for speech synthesis. Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8683143"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1109\/TASLP.2019.2956145","article-title":"Neural Source-Filter Waveform Models for Statistical Parametric Speech Synthesis","volume":"28","author":"Wang","year":"2020","journal-title":"IEEEACM Trans. Audio Speech Lang. Process."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yamamoto, R., Song, E., and Kim, J.M. (2020, January 4\u20138). Parallel wavegan: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram. Proceedings of the 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9053795"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yang, G., Yang, S., Liu, K., Fang, P., Chen, W., and Xie, L. (2021, January 19\u201322). Multi-band melgan: Faster waveform generation for high-quality text-to-speech. Proceedings of the 2021 IEEE Spoken Language Technology Workshop (SLT 2021), Shenzhen, China.","DOI":"10.1109\/SLT48900.2021.9383551"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lorenzo-Trueba, J., Drugman, T., Latorre, J., Merritt, T., Putrycz, B., Barra-Chicote, R., Moinet, A., and Aggarwal, V. (2019, January 15\u201319). Towards Achieving Robust Universal Neural Vocoding. Proceedings of the 20th Annual Conference of the International Speech Communication Association (INTERSPEECH 2019), Graz, Austria.","DOI":"10.21437\/Interspeech.2019-1424"},{"key":"ref_23","unstructured":"Jang, W., Lim, D., and Yoon, J. (2021). Universal MelGAN: A Robust Neural Vocoder for High-Fidelity Waveform Generation in Multiple Domains. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Jiao, Y., Gabry\u015b, A., Tinchev, G., Putrycz, B., Korzekwa, D., and Klimkov, V. (2021, January 6\u201311). Universal neural vocoding with parallel wavenet. Proceedings of the 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2021), Toronto, ON, Canada.","DOI":"10.1109\/ICASSP39728.2021.9414444"},{"key":"ref_25","unstructured":"Ping, W., Peng, K., Gibiansky, A., Arik, S.O., Kannan, A., Narang, S., Raiman, J., and Miller, J. (2018). Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1109\/TASLP.2019.2960721","article-title":"Non-Parallel Sequence-to-Sequence Voice Conversion with Disentangled Linguistic and Speaker Representations","volume":"28","author":"Zhang","year":"2020","journal-title":"IEEEACM Trans. Audio Speech Lang. Process."},{"key":"ref_27","unstructured":"Qian, K., Zhang, Y., Chang, S., Yang, X., and Hasegawa-Johnson, M. (2019, January 9\u201315). AutoVC: Zero-shot voice style transfer with only autoencoder loss. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_28","unstructured":"Qian, K., Zhang, Y., Chang, S., Hasegawa-Johnson, M., and Cox, D. (2020, January 13\u201318). Unsupervised speech decomposition via triple Information Bottleneck. Proceedings of the 37th International Conference on Machine Learning, Virtual."},{"key":"ref_29","unstructured":"Benaroya, L., Obin, N., and Roebel, A. (2021). Beyond Voice Identity Conversion: Manipulating Voice Attributes by Adversarial Learning of Structured Disentangled Representations. arXiv."},{"key":"ref_30","unstructured":"Desler, A. (2014). \u2018Il Novello Orfeo\u2019 Farinelli: Vocal Profile, Aesthetics, Rhetoric. [Ph.D. Thesis, University of Glasgow]."},{"key":"ref_31","unstructured":"Lample, G., Zeghidour, N., Usunier, N., Bordes, A., Denoyer, L., and Ranzato, M. (2017, January 4\u20139). Fader Networks:Manipulating Images by Sliding Attributes. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5464","DOI":"10.1109\/TIP.2019.2916751","article-title":"AttGAN: Facial Attribute Editing by Only Changing What You Want","volume":"28","author":"He","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Collins, E., Bala, R., Price, B., and Susstrunk, S. (2020, January 13\u201319). Editing in style: Uncovering the local semantics of GANs. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00581"},{"key":"ref_34","unstructured":"Engel, J., Hantrakul, L., Gu, C., and Roberts, A. (2020). DDSP: Differentiable Digital Signal Processing. arXiv."},{"key":"ref_35","unstructured":"Engel, J., Hantrakul, L., Gu, C., and Roberts, A. (2021, December 22). DDSP Software. Available online: https:\/\/github.com\/magenta\/ddsp."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"12","DOI":"10.2307\/3680788","article-title":"Spectral Modeling Synthesis: A Sound Analysis\/Synthesis System Based on a Deterministic plus Stochastic Decomposition","volume":"14","author":"Serra","year":"1990","journal-title":"Comput. Music J."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Song, E., Byun, K., and Kang, H.G. (2019, January 2\u20136). ExcitNet vocoder: A neural excitation model for parametric speech synthesis systems. Proceedings of the 2019 IEEE 27th European Signal Processing Conference (EUSIPCO), Coruna, Spain.","DOI":"10.23919\/EUSIPCO.2019.8902701"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1109\/TASLP.2019.2906484","article-title":"GlotNet\u2014A Raw Waveform Model for the Glottal Excitation in Statistical Parametric Speech Synthesis","volume":"27","author":"Juvela","year":"2019","journal-title":"IEEEACM Trans. Audio Speech Lang. Process."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Juvela, L., Bollepalli, B., Yamagishi, J., and Alku, P. (2019, January 15\u201319). GELP: GAN-excited liner prediction for speech synthesis from mel-spectrogram. Proceedings of the International Speech Communication Association (Interspeech 2019), Graz, Austria.","DOI":"10.21437\/Interspeech.2019-2008"},{"key":"ref_40","unstructured":"Oh, S., Lim, H., Byun, K., Hwang, M.J., Song, E., and Kang, H.G. (2020, January 7\u201310). ExcitGlow: Improving a WaveGlow-based Neural Vocoder with Linear Prediction Analysis. Proceedings of the IEEE 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Auckland, New Zealand."},{"key":"ref_41","unstructured":"Roebel, A., and Bous, F. (2021). Towards Universal Neural Vocoding with a Multi-band Excited WaveNet. arXiv."},{"key":"ref_42","unstructured":"Aitken, A., Ledig, C., Theis, L., Caballero, J., Wang, Z., and Shi, W. (2017). Checkerboard Artifact Free Sub-Pixel Convolution: A Note on Sub-Pixel Convolution, Resize Convolution and Convolution Resize. arXiv."},{"key":"ref_43","unstructured":"Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X. (2016, January 5\u201310). Improved techniques for training gans. Proceedings of the Advances in Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_44","unstructured":"Van den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., and Kavukcuoglu, K. (2016, January 13\u201315). WaveNet: A generative model for raw audio. Proceedings of the 9th ISCA Speech Synthesis Workshop, Sunnyvale, CA, USA."},{"key":"ref_45","unstructured":"Smith, J.O. (2021, May 10). Spectral Audio Signal Processing. Available online: https:\/\/ccrma.stanford.edu\/~jos\/sasp."},{"key":"ref_46","first-page":"1343","article-title":"On Cepstral and All-Pole Based Spectral Envelope Modeling with Unknown Model Order","volume":"28","author":"Villavicencio","year":"2007","journal-title":"Pattern Recognit. Lett. Spec. Issue Adv. Pattern Recognit. Speech Audio Process."},{"key":"ref_47","unstructured":"Smith, J.O. (2021, May 10). Introduction to Digital Filters with Audio Applications. Available online: https:\/\/ccrma.stanford.edu\/~jos\/filters\/filters.html."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Yu, C., Lu, H., Hu, N., Yu, M., Weng, C., Xu, K., Liu, P., Tuo, D., Kang, S., and Lei, G. (2020, January 25\u201329). DurIAN: Duration Informed Attention Network for Speech Synthesis. Proceedings of the 21st Annual Conference of the International Speech Communication Association (INTERSPEECH 2020), Shanghai, China.","DOI":"10.21437\/Interspeech.2020-2968"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1109\/97.681427","article-title":"A Kaiser Window Approach for the Design of Prototype Filters of Cosine Modulated Filterbanks","volume":"5","author":"Lin","year":"1998","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/0305-0483(96)00010-2","article-title":"Effect of Data Standardization on Neural Network Training","volume":"24","author":"Shanker","year":"1996","journal-title":"Omega"},{"key":"ref_51","unstructured":"McFee, B. (2021, October 05). Librosa\u2014Librosa 0.8.1 Documentation. Available online: https:\/\/librosa.org\/doc\/latest\/index.html#id1."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1109\/TASSP.1984.1164317","article-title":"Signal Estimation from Modified Short-Time Fourier Transform","volume":"32","author":"Griffin","year":"1984","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_53","unstructured":"Kingma, D.P., and Ba, J.L. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_54","unstructured":"Ito, K., and Johnson, L. (2021, May 10). The LJ Speech Dataset. Available online: https:\/\/keithito.com\/LJ-Speech-Dataset."},{"key":"ref_55","unstructured":"Yamagishi, J., Veaux, C., and MacDonald, K. (2021, October 05). CSTR VCTK Corpus: English Multi-Speaker Corpus for CSTR Voice Cloning Toolkit (Version 0.92). Available online: https:\/\/datashare.ed.ac.uk\/handle\/10283\/3443."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Pirker, G., Wohlmayr, M., Petrik, S., and Pernkopf, F. (2011, January 27\u201331). A Pitch Tracking Corpus with Evaluation on Multipitch Tracking Scenario. Proceedings of the 12th Annual Conference of the International Speech Communication Association (INTERSPEECH 2011), Florence, Italy.","DOI":"10.21437\/Interspeech.2011-317"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Le Moine, C., and Obin, N. (2020, January 25\u201328). Att-HACK: An Expressive Speech Database with Social Attitudes. Proceedings of the 10th International Conference on Speech Prosody, Tokyo, Japan.","DOI":"10.21437\/SpeechProsody.2020-152"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Grammalidis, N., Dimitropoulos, K., Tsalakanidou, F., Kitsikidis, A., Roussel, P., Denby, B., Chawah, P., Buchman, L., Dupont, S., and Laraba, S. (2016, January 5\u20136). The I-treasures intangible cultural heritage dDataset. Proceedings of the 3rd International Symposium on Movement and Computing, Thessaloniki, Greece.","DOI":"10.1145\/2948910.2948944"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Duan, Z., Fang, H., Li, B., Sim, K.C., and Wang, Y. (November, January 29). The NUS sung and spoken lyrics corpus: A quantitative comparison of singing and speech. Proceedings of the 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, Kaohsiung, Taiwan.","DOI":"10.1109\/APSIPA.2013.6694316"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Tsirulnik, L., and Dubnov, S. (2019, January 20\u201325). Singing voice database. Proceedings of the International Conference on Speech and Computer, Istanbul, Turkey.","DOI":"10.1007\/978-3-030-26061-3_51"},{"key":"ref_61","unstructured":"Koguchi, J., Takamichi, S., and Morise, M. (2020, January 7\u201310). PJS: Phoneme-balanced Japanese singing-voice corpus. Proceedings of the 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Auckland, New Zealand."},{"key":"ref_62","unstructured":"Tamaru, H., Takamichi, S., Tanji, N., and Saruwatari, H. (2020). JVS-MuSiC: Japanese Multispeaker Singing-Voice Corpus. arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1250\/ast.42.140","article-title":"Tohoku Kiritan Singing Database: A Singing Database for Statistical Parametric Singing Synthesis Using Japanese Pop Songs","volume":"42","author":"Ogawa","year":"2021","journal-title":"Acoust. Sci. Technol."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Zen, H., Dang, V., Clark, R., Zhang, Y., Weiss, R.J., Jia, Y., Chen, Z., and Wu, Y. (2019). LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech. arXiv.","DOI":"10.21437\/Interspeech.2019-2441"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Ardaillon, L., and Roebel, A. (2019, January 15\u201319). Fully-Convolutional Network for Pitch Estimation of Speech Signals. Proceedings of the 20th Annual Conference of the International Speech Communication Association (INTERSPEECH 2019), Graz, Austria.","DOI":"10.21437\/Interspeech.2019-2815"},{"key":"ref_66","unstructured":"Roebel, A. (2021, January 31). SuperVP Software. Available online: http:\/\/anasynth.ircam.fr\/home\/english\/software\/supervp."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Huber, S., and Roebel, A. (2015, January 6\u201310). On glottal source shape parameter transformation using a novel deterministic and stochastic speech analysis and synthesis system. Proceedings of the 16th Annual Conference of the International Speech Communication Association (Interspeech 2015), Dresden, Germany.","DOI":"10.21437\/Interspeech.2015-119"},{"key":"ref_68","unstructured":"Rafii, Z., Liutkus, A., St\u00f6ter, F.R., Mimilakis, S., and Bittner, R. (2021, January 31). The MUSDB18 Corpus for Music Separation. Available online: https:\/\/sigsep.github.io\/datasets\/musdb.html."},{"key":"ref_69","unstructured":"Roebel, A., and Bous, F. (2022, February 08). MBExWN_Vocoder. Available online: https:\/\/github.com\/roebel\/MBExWN_Vocoder."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Yoneyama, R., Wu, Y.C., and Toda, T. (2021). Unified Source-Filter GAN: Unified Source-filter Network Based On Factorization of Quasi-Periodic Parallel WaveGAN. arXiv.","DOI":"10.21437\/Interspeech.2021-517"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/3\/103\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:25:33Z","timestamp":1760135133000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/3\/103"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,23]]},"references-count":70,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["info13030103"],"URL":"https:\/\/doi.org\/10.3390\/info13030103","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,23]]}}}