musical genres binary classification using pytorch.audio and keras
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Updated
Nov 3, 2020 - Jupyter Notebook
musical genres binary classification using pytorch.audio and keras
Detecting emotions from audios using neural networks
Given mel-frequency spectrogram predict whether audio is clean or noisy
In this project, I implemented Convolutional Neural Networks on images of melspectrogram of sound files.
There are already many projects underway to extensively monitor birds by continuously recording natural soundscapes over long periods. However, as many living and nonliving things make noise, the analysis of these datasets is often done manually by domain experts. These analyses are painstakingly slow, and results are often incomplete.
This repository presents the results of a technological initiation that encouraged my undergraduate thesis, I sought to improve my knowledge in digital signal processing applied to music. It is a spectral analysis tool for the audio of musical instruments, focusing on objectively characterizing timbre.
Fashion Mnist and "recognize a speaker" datasets were utilized for image classification. For this classification task were tried to apply transfer learning from Mnist Fashion to "Recognize a Speaker" and transfer learning inside of Mnist Fashion.
A C++ implementation of stft, melspectrogram and mel_to_stft
Sentiment Analysis from Speech!
An example repository to analyze cough audio data using transfer learning
This project was developed during the course Laboratory of Computational Physics
This repository is mainly about the classification of music genres of Kaggle music dataset in various forms of data preparation
audio classification fastai - Convert audio files into images for classification
Classify audio recordings to a set of emotions.
During the project for the DIGITAL SIGNAL IMAGE MANAGEMENT course I learned how to manage and process audio and image files. The aim of the project was the classification, through machine learning and deep learning models, of musical genres by extracting specific audio features from the "gtzan dataset" dataset files with which to train the model…
Using Deep Convolutional Generative Adversarial Networks to generate new spoken digits.
Fall 2021 Introduction to Deep Learning - Homework 3 Part 2 (RNN-based phoneme recognition)
Classifying Music Genre with Urban Sound Dataset, Preprocessing with Librosa and Torch audio, Model made in Tensorflow and PyTorch
The project aims to represent real time Mel Spectogram of Audio Data fed through Mems microphone on Addressable LEDs (WS2812b).
Audio Based COVID Detector
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