// AUDIO AI
Audio classification pipeline for recognizing animal sounds using YAMNet and classical audio features, optimized for edge deployment.
Developed an end-to-end audio classification pipeline for animal sound recognition using Google's YAMNet alongside MFCC and Mel spectrogram features. The pipeline was optimized for lightweight inference and edge deployment, achieving a recall of 0.846.
Animal sound analysis can require processing large volumes of audio while operating under constrained computational resources. A lightweight classification pipeline is needed to extract meaningful acoustic features and perform reliable recognition on edge devices.
Name
Animal Sound Dataset
Metadata Matrix Size
Not specified
Telemetry Source
Dataset-dependent
Pipeline Preprocessing Steps
Core Pattern
YAMNet + Audio Feature Classification
Technology Stack
TensorFlow
Key Components
Audio Preprocessing
Load and preprocess animal audio recordings for consistent classification input.
Feature Extraction
Extract MFCCs and Mel spectrogram representations from the audio signals.
YAMNet Embeddings
Generate pretrained YAMNet audio embeddings to capture high-level acoustic features.
Classification
Use extracted audio representations for animal sound classification.
Evaluation
Evaluate classification performance with recall as a key metric.
Edge Optimization
Optimize the inference pipeline for deployment on resource-constrained TinyML and edge platforms.
0.846
Recall