gtzan

Pytorch and TensorFlow data loaders for several audio datasets

Hackville 2025 example of CreateML & CoreML for iOS platforms

Clasificación de los géneros musicales utilizando técnicas de aprendizaje profundo (CNN y LSTM) en el conjunto de datos GTZAN. Classification of musical genres using Deep Learning techniques (CNN and LSTM) on the GTZAN dataset.

Music genre classification on the GTZAN dataset

GTZAN - Music Genre Classification

A full-stack multimodal music agent for audio analysis, playlist planning, and LLM-based music recommendation.

A convolutional neural network pipeline for music genre classification. Raw audio files are converted to mel-spectrogram images, which are then used to train and compare three CNN architectures. Includes training curves, confusion matrices, and automatic best-model saving.

Music Genre Classification and Recommendation

A ML approach to find genre of a given song file and to recommend similar songs in the same dataset.

🎵 Music Genre Classification with 90% accuracy on GTZAN dataset using CNNs and mel-spectrograms. State-of-the-art deep learning approach with complete documentation.

Comparative study of six neural network architectures for music genre classification using the GTZAN dataset

Music genre classification with honest, leakage-audited evaluation — SHAP explanations and MMR playlist recommendation over GTZAN. Reports 89.7% / 79.3% / 51.4% across three split protocols instead of just the flattering one.

Audio genre classification using KNN on GTZAN dataset

Music Genre Classification using Logistic Regression

FastAPI service for music genre classification (GTZAN, 10 classes) using a CNN with Mel spectrograms. 78% test accuracy, SpecAugment data augmentation, fully offline inference.

Real-time music-genre classification: spectrogram CNN, ONNX-optimised, served as a streaming/chunked classifier with PyTorch-vs-ONNX benchmarks. Track-aware GTZAN eval.

Comparing SVM and Transformer (AST) models for classifying music genres using the GTZAN dataset.