Results 31 to 40 of about 8,338,200 (223)

MelHuBERT: A Simplified Hubert on Mel Spectrograms [PDF]

2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 2023
ASRU ...
Lin, Tzu-Quan, Lee, Hung-yi, Tang, Hao; id_orcid 0000-0002-2445-2605   +2 more
openaire   +3 more sources

Leveraging MFCC and Mel-Spectrogram Representations for Deep Learning-Based Speech Recognition

Engineering Proceedings
This work propose an audio feature pipeline to support machine learning tasks through the extraction of the Mel Frequency Cepstral Coefficients and Mel-spectrogram which is then used as the input of an Convolutional Neural Network which is trained to ...
Jose Antonio Lopez-Olvera, Hector Manuel Perez-Meana, Elizabeth Garcia-Rios, Enrique Escamilla-Hernandez   +3 more
doaj   +2 more sources

Diagnosis of epileptic seizures from electroencephalogram signals using log-Mel spectrogram and a deep learning CNN model

Heliyon
Tchiotsop Daniel, Beaudelaire SAHA TCHINDA, Laurent Chanel Djoufack Nkengfack   +2 more
exaly   +2 more sources

Multitaper-Mel Spectrograms for Keyword Spotting

IEEE Signal Processing Letters, 2022
Keyword spotting (KWS) is one of the speech recognition tasks most sensitive to the quality of the feature representation. However, the research on KWS has traditionally focused on new model topologies, putting little emphasis on other aspects like feature extraction.
Douglas David Baptista de Souza, Khaled Jamal Bakri, Fernanda de Souza Ferreira, Juliana Camilo Inácio   +3 more
openaire   +2 more sources

Mel Spectrogram Inversion with Stable Pitch

CoRR, 2022
7 pages, 5 figures, Proceedings of the 23st International Society for Music Information Retrieval Conference, ISMIR ...
Bruno Di Giorgi, Mark Levy, Richard Sharp   +2 more
openaire   +4 more sources

Speech Emotion Recognition using Mel Spectrogram and Convolutional Neural Networks (CNN)

Procedia Computer Science
Seeja K R
exaly   +2 more sources

Research on Speech Synthesis Based on Mixture Alignment Mechanism

Sensors, 2023
In recent years, deep learning-based speech synthesis has attracted a lot of attention from the machine learning and speech communities. In this paper, we propose Mixture-TTS, a non-autoregressive speech synthesis model based on mixture alignment ...
Yan Deng, Ning Wu, Chengjun Qiu, Yan Chen, Xueshan Gao   +4 more
doaj   +1 more source

Generating Diverse Vocal Bursts with StyleGAN2 and MEL-Spectrograms

CoRR, 2022
We describe our approach for the generative emotional vocal burst task (ExVo Generate) of the ICML Expressive Vocalizations Competition. We train a conditional StyleGAN2 architecture on mel-spectrograms of preprocessed versions of the audio samples. The mel-spectrograms generated by the model are then inverted back to the audio domain. As a result, our
Marco Jiralerspong, Gauthier Gidel
openaire   +3 more sources

Comparative Study of Popular Deep Learning Models for Machining Roughness Classification Using Sound and Force Signals

Micromachines, 2021
This study compared popular Deep Learning (DL) architectures to classify machining surface roughness using sound and force data. The DL architectures considered in this study include Multi-Layer Perceptron (MLP), Convolution Neural Network (CNN), Long ...
Binayak Bhandari
doaj   +1 more source

Evaluation of Respiratory Sounds Using Image-Based Approaches for Health Measurement Applications

IEEE Open Journal of Engineering in Medicine and Biology, 2022
Goal: The evaluation of respiratory events using audio sensing in an at-home setting can be indicative of worsening health conditions. This paper investigates the use of image-based transfer learning applied to five audio visualizations to evaluate three
Madison Cohen-McFarlane, Pengcheng Xi, Bruce Wallace, Karim Habashy, Saiful Huq, Rafik Goubran, Frank Knoefel   +6 more
doaj   +1 more source