Results 181 to 190 of about 8,338,200 (223)

DMEL: The Differentiable Log-Mel Spectrogram as a Trainable Layer in Neural Networks

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
In this paper we present the differentiable log-Mel spectrogram (DMEL) for audio classification. DMEL uses a Gaussian window, with a window length that can be jointly optimized with the neural network.
John Martinsson, Maria Sandsten
semanticscholar   +5 more sources

Mel Spectrogram-Based CNN Framework for Explainable Audio Deepfake Detection

International Conference on Advanced Information Networking and Applications
The rise of audio deepfakes is becoming a growing concern for media credibility, particularly on social platforms. This study explores an approach to detecting audio deepfakes using Convolutional Neural Networks (CNNs) applied to Mel spectrograms, which serve as visual representations of audio signals.
Muhammad Khurram Zahur Bajwa, Aniello Castiglione, Chiara Pero   +2 more
semanticscholar   +5 more sources

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Underwater target recognition using convolutional recurrent neural networks with 3-D Mel-spectrogram and data augmentation

Applied Acoustics, 2021
Passive recognition of underwater acoustic targets is a hot research issue in acoustic signal processing. The long-term interference of irregular noise in the marine environment caused the relevance of the passive recognition method of underwater targets
Zailei Luo, Tongsheng Shen
exaly   +2 more sources

MelRe: Vision-Based Mel-Spectrogram Restoration

Interspeech 2025
With advancements in visual technology, an increasing number of visual techniques have recently been applied in other fields. Among them, mel spectrograms provide a bridge between audio features and visual models.
Kaixuan Luan, Xiao-Da Yang, Shile Cai, Ruofan Hu, Ming-Hui Fang, Wenrui Liu, Jia-Long Zuo, Jiaqi Duan, Yuhang Ma, JunYu Lu   +9 more
semanticscholar   +2 more sources

Cross-Attention-Guided WaveNet for EEG-to-MEL Spectrogram Reconstruction

Interspeech 2024
This paper introduces an innovative approach that leverages a cross-attention-guided WaveNet combined with a coarse-to-fine granularity strategy to enhance the detailed reconstruction of Mel spectrograms from time-domain EEG signals.
Hao Li, Yuan Fang, Xueliang Zhang, Fei Chen, Guanglai Gao   +4 more
semanticscholar   +2 more sources

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   +2 more sources

Emotion Recognition from Speech Signals by Mel-Spectrogram and a CNN-RNN

2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Speech emotion recognition (SER) in health applications can offer several benefits by providing insights into the emotional well-being of individuals. In this work, we propose a method for SER using time-frequency representation of the speech signals and
R. Sharan, Cecilia Mascolo, Björn W. Schuller   +2 more
semanticscholar   +3 more sources

Heart Sound Classification Using Deep Learning Techniques Based on Log-mel Spectrogram

Circuits, Systems, and Signal Processing, 2022
Minh T Nguyen, Jin H Huang
exaly   +2 more sources

Underwater acoustic target recognition based on sub-band concatenated Mel spectrogram and multidomain attention mechanism

Engineering Applications of Artificial Intelligence
Anqi Jin, Shuang Yang
exaly   +2 more sources

Anomaly identification of wind turbine blades based on Mel-Spectrogram Difference feature of aerodynamic noise

Measurement: Journal of the International Measurement Confederation
Zhuo Wang
exaly   +2 more sources