Papers Audio Classification
“Audio Classification” 태그가 달린 논문 413편 · 필터 해제
A Human-Inspired Decoupled Architecture for Efficient Audio Representation Learning
While self-supervised learning (SSL) has revolutionized audio representation, the excessive parameterization and quadratic computational cost of standard Transformers limit their deployment on resource-constrained device…
Self-Supervised LearningRepresentation LearningKnowledge DistillationAudio ClassificationAbjad-Kids: An Arabic Speech Classification Dataset for Primary Education
Speech-based AI educational applications have gained significant interest in recent years, particularly for children. However, children speech research remains limited due to the lack of publicly available datasets, espe…
Audio ClassificationData AugmentationDendroNN: Dendrocentric Neural Networks for Energy-Efficient Classification of Event-Based Data
Spatiotemporal information is at the core of diverse sensory processing and computational tasks. Feed-forward spiking neural networks can be used to solve these tasks while offering potential benefits in terms of energy …
Audio ClassificationAdaptive Discovery of Interpretable Audio Attributes with Multimodal LLMs for Low-Resource Classification
In predictive modeling for low-resource audio classification, extracting high-accuracy and interpretable attributes is critical. Particularly in high-reliability applications, interpretable audio attributes are indispens…
Audio ClassificationHardware-accelerated graph neural networks: an alternative approach for neuromorphic event-based audio classification and keyword spotting on SoC FPGA
As the volume of data recorded by embedded edge sensors increases, particularly from neuromorphic devices producing discrete event streams, there is a growing need for hardware-aware neural architectures that enable effi…
Audio ClassificationKeyword SpottingCALM: Class-Conditional Sparse Attention Vectors for Large Audio-Language Models
Large audio-language models (LALMs) exhibit strong zero-shot capabilities in multiple downstream tasks, such as audio question answering (AQA) and abstract reasoning; however, these models still lag behind specialized mo…
Audio ClassificationQuestion AnsweringSynthetic Data Augmentation for Medical Audio Classification: A Preliminary Evaluation
Medical audio classification remains challenging due to low signal-to-noise ratios, subtle discriminative features, and substantial intra-class variability, often compounded by class imbalance and limited training data. …
Audio ClassificationData AugmentationBangla Music Genre Classification Using Bidirectional LSTMS
Bangla music is enrich in its own music cultures. Now a days music genre classification is very significant because of the exponential increase in available music, both in digital and physical formats. It is necessary to…
Audio ClassificationGenre classificationSLAP: Scalable Language-Audio Pretraining with Variable-Duration Audio and Multi-Objective Training
Contrastive language-audio pretraining (CLAP) has achieved notable success in learning semantically rich audio representations and is widely adopted for various audio-related tasks. However, current CLAP models face seve…
Audio ClassificationText RetrievalLeveraging Prediction Entropy for Automatic Prompt Weighting in Zero-Shot Audio-Language Classification
Audio-language models have recently demonstrated strong zero-shot capabilities by leveraging natural-language supervision to classify audio events without labeled training data. Yet, their performance is highly sensitive…
Audio ClassificationDHAuDS: A Dynamic and Heterogeneous Audio Benchmark for Test-Time Adaptation
Existing Test-time Adaptation (TTA) studies rely heavily on static and homogeneous corruption protocols, such as ImageNet-C and CIFAR-10-C/100-C, leading to inconsistent evaluation settings and potentially inflated robus…
Test-time AdaptationAudio ClassificationMSMT-FN: Multi-segment Multi-task Fusion Network for Marketing Audio Classification
Audio classification plays an essential role in sentiment analysis and emotion recognition, especially for analyzing customer attitudes in marketing phone calls. Efficiently categorizing customer purchasing propensity fr…
Audio ClassificationEmotion RecognitionSentiment AnalysisUncertainty Makes It Stable: Curiosity-Driven Quantized Mixture-of-Experts
Deploying deep neural networks on resource-constrained devices faces two critical challenges: maintaining accuracy under aggressive quantization while ensuring predictable inference latency. We present a curiosity-driven…
Audio ClassificationAMAuT: A Flexible and Efficient Multiview Audio Transformer Framework Trained from Scratch
Recent foundational models, SSAST, EAT, HuBERT, Qwen-Audio, and Audio Flamingo, achieve top-tier results across standard audio benchmarks but are limited by fixed input rates and durations, hindering their reusability. T…
Test-time AdaptationAudio ClassificationPhase-Aware Deep Learning with Complex-Valued CNNs for Audio Signal Applications
This study explores the design and application of Complex-Valued Convolutional Neural Networks (CVCNNs) in audio signal processing, with a focus on preserving and utilizing phase information often neglected in real-value…
Genre classificationAudio ClassificationUnmute the Patch Tokens: Rethinking Probing in Multi-Label Audio Classification
Although probing frozen models has become a standard evaluation paradigm, self-supervised learning in audio defaults to fine-tuning when pursuing state-of-the-art on AudioSet. A key reason is that global pooling creates …
Self-Supervised LearningAudio ClassificationThinking While Listening: Simple Test Time Scaling For Audio Classification
We propose a framework that enables neural models to "think while listening" to everyday sounds, thereby enhancing audio classification performance. Motivated by recent advances in the reasoning capabilities of large lan…
Audio ClassificationAn 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 ClassificationThreat Modeling for Enhancing Security of IoT Audio Classification Devices under a Secure Protocols Framework
The rapid proliferation of IoT nodes equipped with microphones and capable of performing on-device audio classification exposes highly sensitive data while operating under tight resource constraints. To protect against t…
Audio ClassificationLow-rank surrogate modeling and stochastic zero-order optimization for training of neural networks with black-box layers
The growing demand for energy-efficient, high-performance AI systems has led to increased attention on alternative computing platforms (e.g., photonic, neuromorphic) due to their potential to accelerate learning and infe…
Audio Classification