International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025
p-ISSN: 2395-0072
www.irjet.net
High-Dimensional Audio Genre Classification Using MFCC-Driven MultiLayer Perceptrons Chiranjeevi Vantaku Department of Computer Science & Electronics and Communication Engineering Maharaj Vijayaram Gajapathi Raj College of Engineering (MVGRCE), Vizianagaram, Andhra Pradesh, India ---------------------------------------------------------------------***--------------------------------------------------------------------1.2 Preprocessing Methodology Abstract - Audio data, as a form of unstructured information, presents both opportunities and challenges in the field of machine learning. Classifying such data effectively requires thorough preprocessing to transform raw signals into meaningful features. This project focuses on building a music genre classifier using artificial neural networks, specifically a multi-class model capable of predicting one of ten genres: blues, classical, country, disco, hip-hop, jazz, metal, pop, reggae, and rock. The goal is to develop a probabilistic model that not only identifies the most likely genre but also provides insight into the confidence of its predictions.
Machine learning algorithms cannot directly interpret raw audio data due to its unstructured nature. To bridge this gap, the audio signals must first be transformed into structured numerical representations. In this project, the raw audio files were processed using signal transformation techniques to extract Mel Frequency Cepstral Coefficients (MFCCs) — a widely used feature set for audio classification tasks. MFCCs are particularly suited for music genre recognition because they reflect how humans perceive sound frequencies, emphasizing lower frequencies where the human ear is most sensitive. This perceptual alignment makes MFCCs highly effective for applications where subjective interpretation of sound plays a role.
Key Words: Audio Classification, MFCC, Multilayer Perceptron, Deep Learning, Music Genre Recognition, Neural Networks, Signal Processing, Feature Extraction, PCA, Overfitting Mitigation
1.INTRODUCTION Audio classification has become an increasingly important area within machine learning, with applications spanning diverse industries. It sits at the intersection of signal processing, machine learning, and acoustics, enabling systems to interpret and categorize sound data. From detecting speech and analyzing environmental noise to recognizing emotional tone in human voices, audio classification supports a wide range of real-world use cases and continues to grow in relevance as audio data becomes more prevalent.
1.1 The Dataset
Figure 1: Visual Representation of Audio Signal
A critical foundation for effective audio classification lies in selecting a dataset that accurately represents the problem space. For music genre classification, an ideal dataset should maintain consistent audio quality, standardized file properties, and well-defined genre labels. The GTZAN Genre Collection, curated by George Tzanetakis, meets these requirements and serves as the primary dataset for this project. It consists of 1,000 audio tracks, evenly distributed across 10 distinct genres, with 100 tracks per genre. Each track is a 30-second .WAV file in lossless format, sampled uniformly at 22,050 Hz. This standardized sampling rate ensures reliable feature extraction across all tracks, minimizing discrepancies due to over- or under-sampling.
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2. Methodology 2.1 Technologies Used A range of Python-based libraries were employed to build and evaluate the music genre classification model. NumPy and Pandas served as the core numerical engines, enabling efficient handling of linear algebra operations and highdimensional data structures. For audio preprocessing, the Librosa library was utilized to extract relevant audio features, including MFCCs. Visualization and performance tracking were conducted using Matplotlib, providing clarity on model behavior and data trends. Scikit-learn
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