music genre classification using 2D CNN, 1D CNN - LSTM and Librosa
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Updated
Sep 2, 2020 - Python
music genre classification using 2D CNN, 1D CNN - LSTM and Librosa
Proyecto para finalización de carrera ingenieria electrónica en UTN Haedo. Se implemento un sistema que es capaz de detectar fallas por medio del espectro audible hasta los 20kHz segun las normas de SKF. Se utilizo un ESP32 para el prototipo, en conjunto con una RasberryPI 400 como servidor.
The project involves converting mp3 file to wav and breaking the wav audio into chunks and analyzing the audio chunks using Google's API. The transcript is stored in a transcript.txt file based on these chunks.
This is a repository I have made for simple sound synthesis, recording and analysis.
The normalised aggregated power envelope (nape) is a representation of an audio signal calculated by summing the columns of the short-time Fourier transform (STFT).
Shader-based musical visualizers.
an ASCII keyboard synthesizer developed in python, which creatively explores the 2 DSP techniques: cross correlation and convolution. Output is produced by performing cross correlation/convolution between musical clips and human speech recordings.
Audio tools for audio processing and audio analysis
Python scripts that plot amplitude, spectrograms and chromagrams for a given mono wav audio file
Covid-19 patient identification system using audio recordings
Extracted features and classified GTZAN Dataset via deep neural networks with reduced number of parameters and achieved a maximum of 81.62% classification accuracy using 1D-CNN.
This repository contains the code for the implementation of the paper Automatic Assessment of Speaking Skills Using Aural and Textual Information (Eleftherio, 2021).
Проверка аудиокниг на соответствие требованиям издательства Эксмо
Depression detection detection using text and audio, dry eye test and emoticons module has been added to already existing github library of JARVIS.
A space for the development of audio-related tools.
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