melspectrogram

kapre: Keras Audio Preprocessors

Audio processing by using pytorch 1D convolution network

UrbanSound classification using Convolutional Recurrent Networks in PyTorch

Polish bird species recognition - Bird song analysis and classification with MFCC and CNNs. Trained on EfficientNets with final score 0.88 AUC. Women in Machine Learning & Data Science project.

C/C++实现Python音频处理库librosa中melspectrogram的计算过程

A packaged convolutional voice activity detector for noisy environments.

Learnable STRF, from Riad et al. 2021 JASA

musical genres binary classification using pytorch.audio and keras

Perform three types of feature extraction: STFT, MFCC and MelSpectrogram. Apply CNN/VGG with or without RNN architecture. Able to achieve 95% accuracy.

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.

audio classification fastai - Convert audio files into images for classification

Given mel-frequency spectrogram predict whether audio is clean or noisy

Detecting emotions from audios using neural networks

In this project, I implemented Convolutional Neural Networks on images of melspectrogram of sound files.

Music recommendation using neural network

This repository is mainly about the classification of music genres of Kaggle music dataset in various forms of data preparation

A simple Speaker classifier using Keras

Classify audio recordings to a set of emotions.

Signal Processing with Python and Librosa

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