Attention-guided Spectrogram Sequence Modeling with CNNs for Music Genre Classification
Music genre classification is a critical component of music recommendation systems, generation algorithms, and cultural analytics. In this work, we present an innovative model for classifying music genres using attention-based temporal signature modeling. By processing spectrogram sequences through Convolutional Neural Networks (CNNs) and multi-head attention layers, our approach captures the most temporally significant moments within each piece, crafting a unique "signature" for genre identification. This temporal focus not only enhances classification accuracy but also reveals insights into genre-specific characteristics that can be intuitively mapped to listener perceptions. Our findings offer potential applications in personalized music recommendation systems by highlighting cross-genre similarities and distinctiveness, aligning closely with human musical intuition. This work bridges the gap between technical classification tasks and the nuanced, human experience of genre.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationGenre classificationMusic Genre ClassificationMusic RecommendationRecommendation SystemsMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
ATGNN: Audio Tagging Graph Neural Network
Deep learning models such as CNNs and Transformers have achieved impressive performance for end-to-end audio tagging. Recent works have shown that despite stacking multiple layers, the receptive field of CNNs remains sev…
Audio TaggingGraph Neural NetworkAn overview of neural architectures for self-supervised audio representation learning from masked spectrograms
In recent years, self-supervised learning has amassed significant interest for training deep neural representations without labeled data. One such self-supervised learning approach is masked spectrogram modeling, where t…
Self-Supervised LearningRepresentation LearningAudio ClassificationMulti-View Speech Representation Learning for Parkinson's Disease Detection Using Context-guided Cross-modal Attention
Parkinson's disease (PD) is a progressive neurodegenerative disorder that frequently causes speech impairments associated with hypokinetic dysarthria. As speech production relies on the precise coordination of complex ne…
Representation LearningModeling Musical Onset Probabilities via Neural Distribution Learning
Musical onset detection can be formulated as a time-to-event (TTE) or time-since-event (TSE) prediction task by defining music as a sequence of onset events. Here we propose a novel method to model the probability of ons…
Onset DetectionPredictioniSTFTNet2: Faster and More Lightweight iSTFT-Based Neural Vocoder Using 1D-2D CNN
The inverse short-time Fourier transform network (iSTFTNet) has garnered attention owing to its fast, lightweight, and high-fidelity speech synthesis. It obtains these characteristics using a fast and lightweight 1D CNN …
Speech Synthesis