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Audio Classification
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Audio Classification is a machine learning task that involves identifying and tagging audio signals into different classes or categories. The goal of audio classification is to enable machines to automatically recognize and distinguish between different types of audio, such as music, speech, and environmental sounds.
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MUPAX: Multidimensional Problem Agnostic eXplainable AI
Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons
Fully Few-shot Class-incremental Audio Classification Using Multi-level Embedding Extractor and Ridge Regression Classifier
SSLAM: Enhancing Self-Supervised Models with Audio Mixtures for Polyphonic Soundscapes
Domain Adaptation Method and Modality Gap Impact in Audio-Text Models for Prototypical Sound Classification
Adaptive Differential Denoising for Respiratory Sounds Classification
Spectrotemporal Modulation: Efficient and Interpretable Feature Representation for Classifying Speech, Music, and Environmental Sounds
Patient-Aware Feature Alignment for Robust Lung Sound Classification:Cohesion-Separation and Global Alignment Losses
4,500 Seconds: Small Data Training Approaches for Deep UAV Audio Classification
15,500 Seconds: Lean UAV Classification Leveraging PEFT and Pre-Trained Networks
Large Language Models Implicitly Learn to See and Hear Just By Reading
A Training Framework for Optimal and Stable Training of Polynomial Neural Networks
Can Masked Autoencoders Also Listen to Birds?
Progressive Rock Music Classification
Token Pruning in Audio Transformers: Optimizing Performance and Decoding Patch Importance
CA^2ST: Cross-Attention in Audio, Space, and Time for Holistic Video Recognition
M2D2: Exploring General-purpose Audio-Language Representations Beyond CLAP
Symbolic Audio Classification via Modal Decision Tree Learning
Weakly Supervised Convolutional Dictionary Learning for Multi-Label Classification
Fundamental Survey on Neuromorphic Based Audio Classification
Masked Latent Prediction and Classification for Self-Supervised Audio Representation Learning
CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive Learning
LHGNN: Local-Higher Order Graph Neural Networks For Audio Classification and Tagging
TSPE: Task-Specific Prompt Ensemble for Improved Zero-Shot Audio Classification
Domain-Incremental Learning for Audio Classification
Continual Low-Rank Scaled Dot-product Attention
Raw Audio Classification with Cosine Convolutional Neural Network (CosCovNN)
STREAM: A Universal State-Space Model for Sparse Geometric Data
Classification of Adventitious Sounds Combining Cochleogram and Vision Transformers
Performance Analysis of Hybrid Quantum-Classical Convolutional Neural Networks for Audio Classification
TaxaBind: A Unified Embedding Space for Ecological Applications
A Novel Score-CAM based Denoiser for Spectrographic Signature Extraction without Ground Truth
Audio Classification of Low Feature Spectrograms Utilizing Convolutional Neural Networks
Beyond Speech and More: Investigating the Emergent Ability of Speech Foundation Models for Classifying Physiological Time-Series Signals
Spectral and Rhythm Features for Audio Classification with Deep Convolutional Neural Networks
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