Results 131 to 140 of about 2,773 (174)

Hybrid deep learning and YAMNet features for asthma diagnosis from respiratory sounds. [PDF]

Sci Rep
Shatat GAE, Moustafa HE, Saraya MS, Marzouk AA, Hekal AA.   +4 more
europepmc   +1 more source

High-fidelity neural speech reconstruction through an efficient acoustic-linguistic dual-pathway framework. [PDF]

Elife
Li J, Guo C, Zhang C, Chang EF, Li Y.
europepmc   +1 more source

Recognition of Soccer Player Actions Using a Synchronized Multi-Camera and mm-Wave Radar Platform. [PDF]

Sensors (Basel)
Keyter DB, de Villiers JP.
europepmc   +1 more source

A Robust Deep Learning Approach for COPD Automated Detection. [PDF]

Sensors (Basel)
Xu S, Deo RC, Prasad SS, Barua PD, Soar J, Acharya R.   +5 more
europepmc   +1 more source

Automatic chick cough detection system based on improved audio spectrogram convolutional transformer neural network. [PDF]

Front Vet Sci
Cai B, Zhou B, Kong X, Zhai M.
europepmc   +1 more source

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Musical Genre Classification Using Mel-Spectrograms and Mel-Scalograms

Anais do XVII Congresso Brasileiro de Inteligência Computacional
Musical genre is a category that groups songs with similar characteristics in terms of style, form, instrumentation, rhythm, harmony, lyrics, or cultural function. The task of Musical Genre Classification is extensively studied in the field of Music Information Retrieval (MIR), and several deep learning techniques have been explored to address it.
Luiz Alberto Viana, Beatriz M. Santos, Antonio Carlos Lopes Fernandes Júnior, Eduardo Furtado Simas Filho   +3 more
openaire   +1 more source

The impact of MFCC, spectrogram, and Mel-Spectrogram on deep learning models for Amazigh speech recognition system

International Journal of Speech Technology
Hassan Satori, Naouar Laaidi, Meryam TELMEM   +2 more
exaly   +3 more sources

A Mel Spectrogram Enhancement Paradigm Based on CWT in Speech Synthesis

2024 International Conference on Asian Language Processing (IALP)
Accepted by IALP ...
Huaning Tan
exaly   +4 more sources

CleanMel: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASR

IEEE Transactions on Audio, Speech and Language Processing
In this work, we propose CleanMel, a single-channel Mel-spectrogram denoising and dereverberation network for improving both speech quality and automatic speech recognition (ASR) performance. The proposed network takes as input the noisy and reverberant microphone recording and predicts the corresponding clean Mel-spectrogram.
Pengyu Wang, Xiaofei Li, Nian Shao
exaly   +3 more sources