VQ-VAE for Acoustic Unit Discovery and Voice Conversion
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Updated
Jul 6, 2023 - Python
VQ-VAE for Acoustic Unit Discovery and Voice Conversion
Predicting depression from acoustic features of speech using a Convolutional Neural Network.
A Python library for measuring the acoustic features of speech (simultaneous speech, high entropy) compared to ones of native speech.
Vector-Quantized Contrastive Predictive Coding for Acoustic Unit Discovery and Voice Conversion
Source code complementing our paper for acoustic event classification using convolutional neural networks.
🎵 A repository for manually annotating files to create labeled acoustic datasets for machine learning.
keras_multi_target_signal_recognition Underwater single channel acoustic multiple targets recognition using ResNet, DenseNet, and Complex-Valued convolutional nerual networks. keras-gpu 2.2.4 with tensorflow-gpu 1.12.0 backend.
Tools and functions for neural data processing and analysis in python
Acoustic sentiment analysis for emotion classification
A dynamically adaptable neural network-based replay spoofing attack detection system.
This repository contains a machine learning-based classification system for detecting speech disorders from acoustic and spectrogram features. It includes data preprocessing, feature extraction, and various classification models such as logistic regression, SVM, random forest, and CNNs.
This project analyzes Spanish speech data, focusing on acoustic features and demographics. It includes data cleaning, outlier detection, clustering, and time series modeling (ARIMA, Holt-Winters) to uncover patterns in speech duration and word frequency.
A Python app for classifying voice recordings using KNN and SVM models. Includes a graphical interface for training, evaluating, and classifying audio data with acoustic descriptors. Designed for audio analysis and machine learning experimentation.
ToneArcLib is an open-source audio analysis library that extracts expressive and structural features from music tracks using signal processing and ML techniques. Ideal for researchers, developers, and creatives building AI-driven music tools.
Whisper fine-tuning using Lightning
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