DMF2Mel: A Dynamic Multiscale Fusion Network for EEG-Driven Mel Spectrogram Reconstruction [PDF]
ACM MultimediaDecoding speech from brain signals is a challenging research problem. Although existing technologies have made progress in reconstructing the mel spectrograms of auditory stimuli at the word or letter level, there remain core challenges in the precise ...
Cun-Hang Fan +6 more
semanticscholar +1 more source
ISTFTNET: Fast and Lightweight Mel-Spectrogram Vocoder Incorporating Inverse Short-Time Fourier Transform [PDF]
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2022 In recent text-to-speech synthesis and voice conversion systems, a mel-spectrogram is commonly applied as an intermediate representation, and the necessity for a mel-spectrogram vocoder is increasing.
Takuhiro Kaneko +3 more
semanticscholar +1 more source
Automated Detection of COVID-19 Cough Sound using Mel-Spectrogram Images and Convolutional Neural Network [PDF]
, 2023 COVID-19 is a new disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variant. The initial symptoms of the disease commonly include fever (83-98%), fatigue or myalgia, dry cough (76-82%), and shortness of breath (31-55 ...
Indriani, Fatma +4 more
core +1 more source
Feature selection for emotion recognition in speech: a comparative study of filter and wrapper methods [PDF]
PeerJ Computer ScienceFeature selection is essential for enhancing the performance and reducing the complexity of speech emotion recognition models. This article evaluates various feature selection methods, including correlation-based (CB), mutual information (MI), and ...
Alaa Altheneyan, Aseel Alhadlaq
doaj +2 more sources
Applied Sciences, 2019 We evaluated electrocardiogram (ECG) biometrics using pre-configured models of convolutional neural networks (CNNs) with various time-frequency representations.
Yeong-Hyeon Byeon, Keun-Chang Kwak
doaj +1 more source
Mel frequency spectral domain defenses against adversarial attacks on speech recognition systems [PDF]
JASA Express Letters, 2023 Automatic speech recognition (ASR) systems are vulnerable to adversarial attacks due to their reliance on machine learning models. Many of the defenses explored for defending ASR systems simply adapt defense approaches developed for the image domain ...
Nicholas Mehlman +3 more
doaj +1 more source
A Hybrid CNN and RNN Variant Model for Music Classification
Applied Sciences, 2023 Music genre classification has a significant role in information retrieval for the organization of growing collections of music. It is challenging to classify music with reliable accuracy.
Mohsin Ashraf +6 more
doaj +1 more source
Environment Sound Classification Based on Visual Multi-Feature Fusion and GRU-AWS
IEEE Access, 2020 There are two major questions regarding Environmental Sound Classification (ESC). What is the best audio recognition framework, and what is the most robust audio feature?
Ning Peng +6 more
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Enhancing Embedded Space with Low–Level Features for Speech Emotion Recognition
Applied SciencesThis work proposes an approach that uses a feature space by combining the representation obtained in the unsupervised learning process and manually selected features defining the prosody of the utterances.
Lukasz Smietanka, Tomasz Maka
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ConvConcatNet: A Deep Convolutional Neural Network to Reconstruct Mel Spectrogram from the EEG [PDF]
2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)To investigate the processing of speech in the brain, simple linear models are commonly used to establish a relationship between brain signals and speech features.
Xi-Ran Xu +6 more
semanticscholar +1 more source