This tutorial walks through constructing a complete music‑genre classification project using Python, covering dataset preparation, MFCC feature extraction, K‑Nearest Neighbors implementation, train‑test splitting, model evaluation, and testing on new audio files, all with reproducible code snippets.
This tutorial walks through building a music‑genre classification system using the GTZAN dataset, extracting MFCC features, implementing a K‑Nearest Neighbors classifier in Python, and achieving roughly 70% accuracy on test data.
This article explains the end‑to‑end deep‑learning pipeline for speech‑to‑text, covering audio digitization, preprocessing with librosa, conversion to Mel spectrograms and MFCCs, data augmentation, a CNN‑RNN architecture, CTC loss, decoding strategies and evaluation with word error rate.
This article describes the end‑to‑end pipeline of a voice robot, covering speech framing, feature extraction (FBank, MFCC), the VGGish embedding network, various model architectures, experimental results on accuracy and recall, and future directions for improving sound‑type classification.
This article describes how the 58 used‑car team built a speaker‑verification pipeline—covering data collection, MFCC feature extraction, LSTM and GMM modeling, threshold tuning, multi‑speaker clustering, and deployment results—to automatically block nuisance telemarketing calls while preserving user privacy.
The paper presents a client‑side speech recognizer that uses a compact TensorFlow Lite Inception‑v3 CNN model combined with an optimized MFCC feature pipeline and ARM‑NEON‑accelerated, multi‑threaded processing, achieving low‑latency, high‑accuracy voice recognition on mobile and embedded devices.
