Papers Audio Classification
“Audio Classification” 태그가 달린 논문 413편 · 필터 해제
Comprehensive Evaluation of CNN-Based Audio Tagging Models on Resource-Constrained Devices
Convolutional Neural Networks (CNNs) have demonstrated exceptional performance in audio tagging tasks. However, deploying these models on resource-constrained devices like the Raspberry Pi poses challenges related to com…
Computational EfficiencyAudio ClassificationAudio TaggingSpectral and Rhythm Feature Performance Evaluation for Category and Class Level Audio Classification with Deep Convolutional Neural Networks
Next to decision tree and k-nearest neighbours algorithms deep convolutional neural networks (CNNs) are widely used to classify audio data in many domains like music, speech or environmental sounds. To train a specific C…
Audio ClassificationFull-Frequency Temporal Patching and Structured Masking for Enhanced Audio Classification
Transformers and State-Space Models (SSMs) have advanced audio classification by modeling spectrograms as sequences of patches. However, existing models such as the Audio Spectrogram Transformer (AST) and Audio Mamba (Au…
Audio ClassificationText as Any-Modality for Zero-Shot Classification by Consistent Prompt Tuning
The integration of prompt tuning with multimodal learning has shown significant generalization abilities for various downstream tasks. Despite advancements, existing methods heavily depend on massive modality-specific la…
Video ClassificationAudio ClassificationImage ClassificationAdaptive Sparse Softmax: An Effective and Efficient Softmax Variant
Softmax with the cross entropy loss is the standard configuration for current neural classification models. The gold score for a target class is supposed to be 1, but it is never reachable under the softmax schema. Such …
Audio ClassificationImage ClassificationImproving Audio Classification by Transitioning from Zero- to Few-Shot
State-of-the-art audio classification often employs a zero-shot approach, which involves comparing audio embeddings with embeddings from text describing the respective audio class. These embeddings are usually generated …
Contrastive LearningAudio ClassificationAn Investigation of Test-time Adaptation for Audio Classification under Background Noise
Domain shift is a prominent problem in Deep Learning, causing a model pre-trained on a source dataset to suffer significant performance degradation on test datasets. This research aims to address the issue of audio class…
Test-time AdaptationAudio ClassificationAdversarial Training Improves Generalization Under Distribution Shifts in Bioacoustics
Adversarial training is a promising strategy for enhancing model robustness against adversarial attacks. However, its impact on generalization under substantial data distribution shifts in audio classification remains la…
Adversarial RobustnessAudio ClassificationTask-Specific Audio Coding for Machines: Machine-Learned Latent Features Are Codes for That Machine
Neural audio codecs, leveraging quantization algorithms, have significantly impacted various speech/audio tasks. While high-fidelity reconstruction is paramount for human perception, audio coding for machines (ACoM) prio…
Audio ClassificationAutomatic Speech RecognitionQuantizationspeech-recognition+1MUPAX: Multidimensional Problem Agnostic eXplainable AI
Robust XAI techniques should ideally be simultaneously deterministic, model agnostic, and guaranteed to converge. We propose MULTIDIMENSIONAL PROBLEM AGNOSTIC EXPLAINABLE AI (MUPAX), a deterministic, model agnostic expla…
Anatomical Landmark DetectionAudio ClassificationBenchmarkingFeature Importance+3Developing an AI-Guided Assistant Device for the Deaf and Hearing Impaired
This study aims to develop a deep learning system for an accessibility device for the deaf or hearing impaired. The device will accurately localize and identify sound sources in real time. This study will fill an importa…
Audio ClassificationVisual LocalizationObject DetectionAudioMAE++: learning better masked audio representations with SwiGLU FFNs
Masked Autoencoders (MAEs) trained on audio spectrogram patches have emerged as a prominent approach for learning self-supervised audio representations. While several recent papers have evaluated key aspects of training …
Audio ClassificationASDA: Audio Spectrogram Differential Attention Mechanism for Self-Supervised Representation Learning
In recent advancements in audio self-supervised representation learning, the standard Transformer architecture has emerged as the predominant approach, yet its attention mechanism often allocates a portion of attention w…
Environmental Sound ClassificationRepresentation LearningAudio ClassificationKeyword SpottingmGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling
Multi-timescale sequence modeling relies on capturing both local fast dynamics and global slow context; yet, maintaining these capabilities under the strict memory constraints common to edge devices remains an open chall…
Audio ClassificationNeuromorphic Wireless Split Computing with Resonate-and-Fire Neurons
Neuromorphic computing offers an energy-efficient alternative to conventional deep learning accelerators for real-time time-series processing. However, many edge applications, such as wireless sensing and audio recogniti…
Audio ClassificationFully Few-shot Class-incremental Audio Classification Using Multi-level Embedding Extractor and Ridge Regression Classifier
In the task of Few-shot Class-incremental Audio Classification (FCAC), training samples of each base class are required to be abundant to train model. However, it is not easy to collect abundant training samples for many…
Audio ClassificationAdaptive Differential Denoising for Respiratory Sounds Classification
Automated respiratory sound classification faces practical challenges from background noise and insufficient denoising in existing systems. We propose Adaptive Differential Denoising network, that integrates noise suppre…
Audio ClassificationClassificationDenoisingDiagnostic+1Spectrotemporal Modulation: Efficient and Interpretable Feature Representation for Classifying Speech, Music, and Environmental Sounds
Audio DNNs have demonstrated impressive performance on various machine listening tasks; however, most of their representations are computationally costly and uninterpretable, leaving room for optimization. Here, we propo…
Audio ClassificationPatient-Aware Feature Alignment for Robust Lung Sound Classification:Cohesion-Separation and Global Alignment Losses
Lung sound classification is vital for early diagnosis of respiratory diseases. However, biomedical signals often exhibit inter-patient variability even among patients with the same symptoms, requiring a learning approac…
Audio ClassificationLung Sound ClassificationSound Classification4,500 Seconds: Small Data Training Approaches for Deep UAV Audio Classification
Unmanned aerial vehicle (UAV) usage is expected to surge in the coming decade, raising the need for heightened security measures to prevent airspace violations and security threats. This study investigates deep learning …
Audio Classificationparameter-efficient fine-tuning