melspectrogram-classifications

There are 6 repositories under melspectrogram-classifications topic.

  • ALaks96/signalML

    @CoditEU Project to train a real time anomaly detection model. MelSpectrogram extraction from .wav -> Data augmentation (rolling, stretching, etc...) -> Generating multi class label (specific failure) with PCA+KMeans -> CNN training on multiclass labeled MelSpectrograms -> Configuring HTTP endpoint with Flask & deploying with Docker -> Uploading to ACR (Azure Container Registry) & testing with ACI (Azure Container Instance)

    Language:Python2300

  • dariodellamura/Classification-of-musical-genres-and-music-retrieval

    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 models (SVM, Linear Regression, Decision tree , Random Forest, Neural Network). Mel spectograms were also extracted to train convolutional neural network models. In addition, the extracted audio features have been used to develop a model of music retrieval which given an audio track in input produces as output 5 audio tracks recommended meiante the use of cousine similarity.

    Language:Jupyter Notebook2100

  • Sk-singla/Music-Genre-Classification-Techniques

    Machine Learning and Deep learning techniques to Classify Music Genre

    Language:Jupyter Notebook2101

  • ryanquinnnelson/CMU-11685-Frame-Level-Classification-of-Speech-using-Deep-Learning

    Fall 2021 Introduction to Deep Learning - Homework 1 Part 2 (Frame Level Classification of Speech)

    Language:Python1200

  • codeiaks/CNN-SpokenDigits-Images

    MelSpectrogram Classifications of Spoken Digits

    Language:MATLAB0100

  • coderjolly/bird-call

    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.

    Language:Python0200