tyiannak/pyAudioAnalysis

Features

• Python Audio Manipulation Libraries - Provides a comprehensive Python library for extracting features, classifying segments, and performing signal processing on audio data.
• Audio and Activity Classifiers - Categorizes unknown sound recordings by training models to recognize specific acoustic patterns.
• Classification Frameworks - Ships a framework for training and evaluating machine learning models to categorize sound recordings based on acoustic patterns.
• Supervised Learning Models - Implements predictive models that assign labels or numerical values to audio data based on extracted acoustic features.
• Supervised Learning - Uses labeled audio data to train models for classifying acoustic patterns or estimating continuous values.
• Audio Spectral Features - Extracts frequency-domain characteristics such as spectrograms to describe the sonic properties of audio signals.
• Audio Classifiers - Implements a framework for training and evaluating statistical models to categorize sound recordings based on learned acoustic patterns.
• Audio Event Classification - Implements a system to categorize audio segments into predefined sound categories using machine learning models.
• Audio Event Detection - Identifies specific non-speech acoustic events within audio recordings to partition them into meaningful sections.
• Audio Feature Extraction - Calculates mathematical representations like spectrograms to describe the sonic characteristics of a recording.
• Audio Segmentation Utilities - Partitions sound recordings into meaningful sections by detecting specific events and removing silence.
• Fourier Transforms - Converts time-domain audio signals into frequency representations to analyze spectral characteristics.
• Dimensionality Reduction - Implements techniques to simplify complex audio datasets by extracting essential structures for content similarity mapping.
• Dimensionality Reduction - Maps similarities in high-dimensional sound data to identify patterns and clusters using dimensionality reduction.
• Cluster Visualizations - Projects high-dimensional audio feature sets into two or three dimensions to reveal clusters and patterns.
• Audio Command-Line Tools - Exposes internal analysis and feature extraction functions through a command line interface for batch processing audio files.
• Continuous Value Regression - Estimates continuous values from audio data, such as measuring the intensity of an emotion.
• Similarity Mapping - Maps content similarities using dimensionality reduction to display patterns and clusters within sound data.
• Event-Based Segmentation - Includes utilities for audio segmentation that allow for the removal of silence and the detection of specific audio events.
• Dimensionality Reduction Visualization - Provides data visualization capabilities that use dimensionality reduction to map content similarities and identify clusters within sound data.
• Temporal Windowing - Divides continuous audio streams into overlapping frames to track how sound features evolve over time.
• Frameworks And Toolkits - Audio analysis library for feature extraction and segmentation.
• Speech Processing - Tools for feature extraction, classification, and diarization.
• Audio Feature Extraction - Feature extraction, classification, and segmentation for audio signals.
• Audio Processing - Feature extraction and classification for audio signals.
• Audio Processing and Analysis - Python library for audio segmentation and spectral feature extraction.

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