该项目使用 TensorFlow 实现音频分类模型来对音频片段进行分类。它特别专注于从录音中识别特定声音

# TensorFlow-Based-Audio-Classifier ## Project Overview This project implements an audio classification model using TensorFlow to classify audio clips. It specifically focuses on identifying specific sounds (for example, Capuchin call detections in a forest environment) from audio recordings. The notebook demonstrates the entire workflow, including data loading, preprocessing (converting audio waveforms to spectrograms), model building, training, and making predictions on new audio clips. ## Installation ### Prerequisites - Python 3.8+ - pip ### Dependencies The project requires various libraries, including TensorFlow, librosa, matplotlib, and others as specified in the notebook. ## To use this classifier, follow these steps: - Data Loading: Load your audio data. Modify the data loading functions according to your dataset structure. - Preprocessing: Convert audio clips into spectrograms for model training. - Model Training: Train the audio classification model using the prepared dataset. - Evaluation and Prediction: Evaluate the model's performance and make predictions on new audio clips. - Refer to the Jupyter notebook AudioClassification.ipynb for detailed code and explanations.