Papers Audio Classification
“Audio Classification” 태그가 달린 논문 413편 · 필터 해제
Few-Shot Open-Set Audio Classification via Transductive Prototype Refinement and Class Logit Enhancement
Few-shot Open-set audio classification requires classifying query samples from known classes with a few labeled support samples while rejecting query samples from unknown classes. Transductive inference jointly observes …
Audio ClassificationEdgeFaaS: A Function-based Framework for Edge Computing
Edge computing brings unique challenges as the resources on the edge are highly diverse in capabilities and capacities, and highly distributed across many users and the physical world. Existing distributed computing fram…
Audio ClassificationFederated LearningTriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios
There are some datasets of varying scales for audio classification (AC) applied to different tasks. However, annotated data is limited for most scenarios, such as domestic environments. To address this challenge, we prop…
Audio ClassificationA Multi-Branch Hierarchy-Aware Framework for Heterogeneous Audio Classification
This technical report describes our system for Task 1 of the DCASE 2026 Challenge, which aims to classify heterogeneous audio recordings according to the Broad Sound Taxonomy (BST). The task requires both accurate second…
Audio ClassificationFew-Shot Open-Set Audio Classification Using Attention Information-Fused Prototypes
Most existing audio classification methods suppose that each query (testing) sample belongs to a class of support (training) samples, and misrecognize samples of unseen classes as seen classes (cannot reject samples of u…
Audio ClassificationZEBRA: Zero-Shot Entropy-Regularized Prompt Learning for Base-to-Novel Generalization in Audio-Language Models
Audio-Language Models (ALMs) achieve strong zero-shot performance by aligning audio with textual class descriptions. Although prompt learning improves accuracy on base classes through few-shot supervised adaptation, we o…
Audio ClassificationDomain-incremental audio classification using domain-specific experts and prototype classifier
This technical report presents submission systems for Task 7(domain-incremental audio classification) of the DCASE 2026 Challenge. The main obstacle is that, the system is unable to access to past or future domain's data…
Incremental LearningAudio ClassificationExploiting Neural Audio Codec Latents for Adversarial Audio Attacks
Deep learning-based audio classification systems, including automatic speaker verification, are vulnerable to adversarial attacks. Realistic real-time threat assessment remains difficult because optimization-based method…
Speaker VerificationAudio ClassificationAcoustic Prompting via Stage-wise Modulation for Few-Shot Learning in Audio Language Models
Audio-Language Models (ALMs) have shown remarkable success in zero-shot audio classification by aligning audio waveforms with text. Recent efforts to improve downstream performance focus on learning optimal text prompts.…
Audio ClassificationFew-Shot LearningCREST: Deployment-Realistic Hardware-in-the-Loop NAS for Embedded Sensing Systems
Deploying neural networks on low-power microcontrollers (MCUs) requires selecting model architectures under tight memory, latency, and energy constraints. Existing workflows often simplify this process along one or more …
Neural Architecture SearchAudio ClassificationFew-shot Class-variable Incremental Audio Classification via Prototype Adaptation and Pseudo Class-variable Training
In the task of few-shot class-incremental audio classification, the number of classes is assumed to always increase without considering the possibility of decrease. However, the number of classes generally increases or d…
Audio ClassificationDrift-Augmented Scoring: Text-Derived Noise Robustness for Zero-Shot Audio-Language Classification
Contrastive audio-language models such as CLAP enable zero-shot audio classification: a sound is labelled by matching its embedding to text prompt embeddings, with no labelled audio. This matching breaks down under acous…
Audio ClassificationRepresentation Matters in Randomized Smoothing for Audio Classification
Randomized smoothing (RS) certifies robustness in the vector space where Gaussian noise is added. In audio classification, this space is often not uniquely defined as standard pipelines normalize, range-control, and tran…
Audio ClassificationKeyword SpottingScalable neuromorphic computing from autonomous spiking dynamics in a clockless reconfigurable chip
We propose a scalable neuromorphic architecture based on spiking dynamics emerging from the autonomous time-continuous evolution of clockless (asynchronous) digital circuits. Implemented on commercially available field-p…
Audio ClassificationProtoSSL: Interpretable Prototype Learning from Unlabeled Time-Series Data
In time-series domains where both predictive performance and interpretability are essential, deep neural networks achieve strong results but provide limited insight into how their predictions are made. Projection-based p…
Audio ClassificationExplicit Dropout: Deterministic Regularization for Transformer Architectures
Dropout is a widely used regularization technique in deep learning, but its effects are typically realized through stochastic masking rather than explicit optimization objectives. We propose a deterministic formulation t…
Audio ClassificationImage ClassificationAction DetectionIncremental learning for audio classification with Hebbian Deep Neural Networks
The ability of humans for lifelong learning is an inspiration for deep learning methods and in particular for continual learning. In this work, we apply Hebbian learning, a biologically inspired learning process, to soun…
Incremental LearningAudio ClassificationContinual LearningCombining Convolution and Delay Learning in Recurrent Spiking Neural Networks
Spiking neural networks (SNNs) are rapidly gaining momentum as an alternative to conventional artificial neural networks in resource constrained edge systems. In this work, we continue a recent research line on recurrent…
Audio ClassificationAdaptive Test-Time Scaling for Zero-Shot Respiratory Audio Classification
Automated respiratory audio analysis promises scalable, non-invasive disease screening, yet progress is limited by scarce labeled data and costly expert annotation. Zero-shot inference eliminates task-specific supervisio…
Audio ClassificationQuantum Vision Theory Applied to Audio Classification for Deepfake Speech Detection
We propose Quantum Vision (QV) theory as a new perspective for deep learning-based audio classification, applied to deepfake speech detection. Inspired by particle-wave duality in quantum physics, QV theory is based on t…
Audio Deepfake DetectionAudio ClassificationImage Classification