Papers Acoustic Scene Classification
“Acoustic Scene Classification” 태그가 달린 논문 140편 · 필터 해제
Device Invariance using Domain Adaptation on Acoustic Scene Classification
This paper explores the effectiveness of domain adaptation techniques when using convolutional neural network (CNN)-based and transformer-based feature representations for acoustic scene classification. Two well-known do…
Acoustic Scene ClassificationDomain AdaptationCross-Cultural Bias in Mel-Scale Representations: Evidence and Alternatives from Speech and Music
Modern audio systems universally employ mel-scale representations derived from 1940s Western psychoacoustic studies, potentially encoding cultural biases that create systematic performance disparities. We present a compr…
Acoustic Scene ClassificationSpeech RecognitionFrom Diet to Free Lunch: Estimating Auxiliary Signal Properties using Dynamic Pruning Masks in Speech Enhancement Networks
Speech Enhancement (SE) in audio devices is often supported by auxiliary modules for Voice Activity Detection (VAD), SNR estimation, or Acoustic Scene Classification to ensure robust context-aware behavior and seamless u…
Acoustic Scene ClassificationSpeech EnhancementActivity DetectionDDSC: Dynamic Dual-Signal Curriculum for Data-Efficient Acoustic Scene Classification under Domain Shift
Acoustic scene classification (ASC) suffers from device-induced domain shift, especially when labels are limited. Prior work focuses on curriculum-based training schedules that structure data presentation by ordering or …
Acoustic Scene ClassificationLightweight and Generalizable Acoustic Scene Representations via Contrastive Fine-Tuning and Distillation
Acoustic scene classification (ASC) models on edge devices typically operate under fixed class assumptions, lacking the transferability needed for real-world applications that require adaptation to new or refined acousti…
Acoustic Scene ClassificationAn Entropy-Guided Curriculum Learning Strategy for Data-Efficient Acoustic Scene Classification under Domain Shift
Acoustic Scene Classification (ASC) faces challenges in generalizing across recording devices, particularly when labeled data is limited. The DCASE 2024 Challenge Task 1 highlights this issue by requiring models to learn…
Acoustic Scene ClassificationData AugmentationAdaptive Knowledge Distillation using a Device-Aware Teacher for Low-Complexity Acoustic Scene Classification
In this technical report, we describe our submission for Task 1, Low-Complexity Device-Robust Acoustic Scene Classification, of the DCASE 2025 Challenge. Our work tackles the dual challenges of strict complexity constrai…
Acoustic Scene ClassificationKnowledge DistillationAuditory Intelligence: Understanding the World Through Sound
Recent progress in auditory intelligence has yielded high-performing systems for sound event detection (SED), acoustic scene classification (ASC), automated audio captioning (AAC), and audio question answering (AQA). Yet…
Acoustic Scene ClassificationSound Event DetectionQuestion AnsweringAudio captioningLow-Complexity Acoustic Scene Classification with Device Information in the DCASE 2025 Challenge
This paper presents the Low-Complexity Acoustic Scene Classification with Device Information Task of the DCASE 2025 Challenge and its baseline system. Continuing the focus on low-complexity models, data efficiency, and d…
Acoustic Scene ClassificationScene ClassificationTransfer LearningImproving Acoustic Scene Classification with City Features
Acoustic scene recordings are often collected from a diverse range of cities. Most existing acoustic scene classification (ASC) approaches focus on identifying common acoustic scene patterns across cities to enhance gene…
Acoustic Scene ClassificationClassificationKnowledge DistillationScene ClassificationCreating a Good Teacher for Knowledge Distillation in Acoustic Scene Classification
Knowledge Distillation (KD) is a widespread technique for compressing the knowledge of large models into more compact and efficient models. KD has proved to be highly effective in building well-performing low-complexity …
Acoustic Scene ClassificationKnowledge DistillationScene ClassificationVariational Bayesian Adaptive Learning of Deep Latent Variables for Acoustic Knowledge Transfer
In this work, we propose a novel variational Bayesian adaptive learning approach for cross-domain knowledge transfer to address acoustic mismatches between training and testing conditions, such as recording devices and e…
Acoustic Scene ClassificationScene ClassificationSpoken Command RecognitionTransfer Learning+1Quantum-Enhanced Transformers for Robust Acoustic Scene Classification in IoT Environments
The proliferation of Internet of Things (IoT) devices equipped with acoustic sensors necessitates robust acoustic scene classification (ASC) capabilities, even in noisy and data-limited environments. Traditional machine …
Acoustic Scene ClassificationData AugmentationScene ClassificationImproving Acoustic Scene Classification in Low-Resource Conditions
Acoustic Scene Classification (ASC) identifies an environment based on an audio signal. This paper explores ASC in low-resource conditions and proposes a novel model, DS-FlexiNet, which combines depthwise separable convo…
Acoustic Scene ClassificationClassificationData AugmentationKnowledge Distillation+3Neurobench: DCASE 2020 Acoustic Scene Classification benchmark on XyloAudio 2
XyloAudio is a line of ultra-low-power audio inference chips, designed for in- and near-microphone analysis of audio in real-time energy-constrained scenarios. Xylo is designed around a highly efficient integer-logic pro…
Acoustic Scene ClassificationScene ClassificationData Efficient Acoustic Scene Classification using Teacher-Informed Confusing Class Instruction
In this technical report, we describe the SNTL-NTU team's submission for Task 1 Data-Efficient Low-Complexity Acoustic Scene Classification of the detection and classification of acoustic scenes and events (DCASE) 2024 c…
Acoustic Scene ClassificationData AugmentationDiversityKnowledge Distillation+1Audio Enhancement for Computer Audition -- An Iterative Training Paradigm Using Sample Importance
Neural network models for audio tasks, such as automatic speech recognition (ASR) and acoustic scene classification (ASC), are susceptible to noise contamination for real-life applications. To improve audio quality, an e…
Acoustic Scene ClassificationAutomatic Speech RecognitionAutomatic Speech Recognition (ASR)Emotion Recognition+4Online Domain-Incremental Learning Approach to Classify Acoustic Scenes in All Locations
In this paper, we propose a method for online domain-incremental learning of acoustic scene classification from a sequence of different locations. Simply training a deep learning model on a sequence of different location…
Acoustic Scene ClassificationAllIncremental LearningScene ClassificationLow-Complexity Acoustic Scene Classification Using Parallel Attention-Convolution Network
This work is an improved system that we submitted to task 1 of DCASE2023 challenge. We propose a method of low-complexity acoustic scene classification by a parallel attention-convolution network which consists of four m…
Acoustic Scene ClassificationData AugmentationKnowledge DistillationScene ClassificationData-Efficient Low-Complexity Acoustic Scene Classification in the DCASE 2024 Challenge
This article describes the Data-Efficient Low-Complexity Acoustic Scene Classification Task in the DCASE 2024 Challenge and the corresponding baseline system. The task setup is a continuation of previous editions (2022 a…
Acoustic Scene ClassificationScene Classification