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his project will serve as an introduction to audio processing, feature extraction, and machine learning, paving the way for more advanced applications such as deep learning-based emotion recognition systems.

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HOORIAGABA/Speech-Emotion-Recognition

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Speech Emotion Recognition with Librosa

Introduction

In the digital age, understanding human emotions through speech can transform a wide range of applications, from improving customer service interactions to supporting mental health monitoring. This Speech Emotion Recognition with Librosa project is designed to leverage audio processing techniques to detect and classify emotions from speech. Using Python libraries like Librosa, Soundfile, and sklearn, this project introduces core concepts in audio processing, feature extraction, and machine learning, providing a foundation for more advanced models, such as those based on deep learning.

Objectives

  • Load and process sound files using Librosa and Soundfile libraries.
  • Perform feature extraction from audio data.
  • Train a Multi-Layer Perceptron (MLP) classifier model using sklearn to recognize emotions in speech.
  • Gain foundational knowledge in audio processing and machine learning to enable further advancements in speech emotion recognition.

Methodology

  1. Data Collection and Preprocessing

    • Collect a dataset of speech recordings labeled with corresponding emotions.
    • Load and preprocess these sound files using the Librosa and Soundfile libraries to prepare them for feature extraction.
  2. Feature Extraction

    • Extract relevant audio features such as Mel-frequency cepstral coefficients (MFCCs), chroma, and spectral contrast using Librosa.
    • Compile these features into a structured format suitable for input into the MLP classifier.
  3. Model Training

    • Use the sklearn library to define and train a Multi-Layer Perceptron (MLP) classifier on the extracted features.
    • Implement cross-validation and parameter tuning to optimize the model's performance.
  4. Evaluation and Analysis

    • Evaluate the trained MLP classifier using appropriate metrics such as accuracy, precision, recall, and F1-score.
    • Analyze results to identify the model's strengths and limitations in recognizing speech emotions.

Tools and Technologies

  • Librosa: For audio processing and feature extraction.
  • Soundfile: For reading and writing sound files.
  • sklearn: For machine learning model development and evaluation.
  • Python: As the programming language to integrate these libraries and implement the project.

How to Run the Project

  1. Clone the repository:
    git clone https://github.com/HOORIAGABA/Speech-Emotion-Recognition-with-Librosa.git
    cd Speech-Emotion-Recognition-with-Librosa

About

his project will serve as an introduction to audio processing, feature extraction, and machine learning, paving the way for more advanced applications such as deep learning-based emotion recognition systems.

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