Papers Audio Classification
“Audio Classification” 태그가 달린 논문 413편 · 필터 해제
Audio Contrastive based Fine-tuning
Audio classification plays a crucial role in speech and sound processing tasks with a wide range of applications. There still remains a challenge of striking the right balance between fitting the model to the training da…
Audio ClassificationContrastive LearningImproving Speech Recognition for African American English With Audio Classification
Automatic speech recognition (ASR) systems have been shown to have large quality disparities between the language varieties they are intended or expected to recognize. One way to mitigate this is to train or fine-tune mo…
Audio ClassificationAutomatic Speech RecognitionAutomatic Speech Recognition (ASR)Form+2Exploring Meta Information for Audio-based Zero-shot Bird Classification
Advances in passive acoustic monitoring and machine learning have led to the procurement of vast datasets for computational bioacoustic research. Nevertheless, data scarcity is still an issue for rare and underrepresente…
Audio ClassificationZero-shot Audio ClassificationZero-Shot LearningDiverse Audio Embeddings-- Bringing Features Back Outperforms CLAP !
With the advent of modern AI architectures, a shift has happened towards end-to-end architectures. This pivot has led to neural architectures being trained without domain-specific biases/knowledge, optimized according to…
Audio ClassificationEDAC: Efficient Deployment of Audio Classification Models For COVID-19 Detection
The global spread of COVID-19 had severe consequences for public health and the world economy. The quick onset of the pandemic highlighted the potential benefits of cheap and deployable pre-screening methods to monitor t…
Audio ClassificationNetwork PruningAudRandAug: Random Image Augmentations for Audio Classification
Data augmentation has proven to be effective in training neural networks. Recently, a method called RandAug was proposed, randomly selecting data augmentation techniques from a predefined search space. RandAug has demons…
Audio ClassificationClassificationData AugmentationLearning Speech Representation From Contrastive Token-Acoustic Pretraining
For fine-grained generation and recognition tasks such as minimally-supervised text-to-speech (TTS), voice conversion (VC), and automatic speech recognition (ASR), the intermediate representations extracted from speech s…
Audio ClassificationAutomatic Speech RecognitionAutomatic Speech Recognition (ASR)Contrastive Learning+6Real-time Detection of AI-Generated Speech for DeepFake Voice Conversion
There are growing implications surrounding generative AI in the speech domain that enable voice cloning and real-time voice conversion from one individual to another. This technology poses a significant ethical threat an…
Audio ClassificationBinary ClassificationDeepFake DetectionFace Swapping+5LCANets++: Robust Audio Classification using Multi-layer Neural Networks with Lateral Competition
Audio classification aims at recognizing audio signals, including speech commands or sound events. However, current audio classifiers are susceptible to perturbations and adversarial attacks. In addition, real-world audi…
Audio ClassificationBAVS: Bootstrapping Audio-Visual Segmentation by Integrating Foundation Knowledge
Given an audio-visual pair, audio-visual segmentation (AVS) aims to locate sounding sources by predicting pixel-wise maps. Previous methods assume that each sound component in an audio signal always has a visual counterp…
Audio ClassificationSegmentationAudioFormer: Audio Transformer learns audio feature representations from discrete acoustic codes
We propose a method named AudioFormer,which learns audio feature representations through the acquisition of discrete acoustic codes and subsequently fine-tunes them for audio classification tasks. Initially,we introduce …
Audio ClassificationClassificationLanguage ModelingLanguage Modelling+1AsthmaSCELNet: A Lightweight Supervised Contrastive Embedding Learning Framework For Asthma Classification Using Lung Sounds
Asthma is one of the most prevalent respiratory disorders, which can be identified by different modalities such as speech, wheezing of lung sounds (LSs), spirometric measures, etc. In this paper, we propose AsthmaSCELNet…
Asthmatic Lung Sound ClassificationAudio ClassificationClassificationDiagnostic+3Detection of Children Abuse by Voice and Audio Classification by Short-Time Fourier Transform Machine Learning implemented on Nvidia Edge GPU device
The safety of children in children home has become an increasing social concern, and the purpose of this experiment is to use machine learning applied to detect the scenarios of child abuse to increase the safety of chil…
Abuse DetectionAudio ClassificationGPUimage-classification+1FlexiAST: Flexibility is What AST Needs
The objective of this work is to give patch-size flexibility to Audio Spectrogram Transformers (AST). Recent advancements in ASTs have shown superior performance in various audio-based tasks. However, the performance of …
Audio ClassificationGlobal birdsong embeddings enable superior transfer learning for bioacoustic classification
Automated bioacoustic analysis aids understanding and protection of both marine and terrestrial animals and their habitats across extensive spatiotemporal scales, and typically involves analyzing vast collections of acou…
Audio ClassificationDecision MakingTransfer LearningAsTFSONN: A Unified Framework Based on Time-Frequency Domain Self-Operational Neural Network for Asthmatic Lung Sound Classification
Asthma is one of the most severe chronic respiratory diseases which can be diagnosed using several modalities, such as lung function test or spirometric measures, peak flow meter-based measures, sputum eosinophils, patho…
Asthmatic Lung Sound ClassificationAudio ClassificationDiagnosticLung Disease Classification+3RDLINet: A Novel Lightweight Inception Network for Respiratory Disease Classification Using Lung Sounds
Respiratory diseases are the world’s third leading cause of mortality. Early detection is critical in dealing with respiratory diseases, as it improves the effectiveness of intervention, including treatment and reducing …
Audio ClassificationClassificationDiagnosticLung Disease Classification+1Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings
Spiking Neural Networks (SNNs) are a promising research direction for building power-efficient information processing systems, especially for temporal tasks such as speech recognition. In SNNs, delays refer to the time n…
Audio Classificationspeech-recognitionSpeech RecognitionS-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural Networks
Spiking Neural Networks (SNNs) are biologically plausible models that have been identified as potentially apt for deploying energy-efficient intelligence at the edge, particularly for sequential learning tasks. However, …
Audio ClassificationEvent-based Optical FlowGesture RecognitionImage Classification+1Audio Tagging on an Embedded Hardware Platform
Convolutional neural networks (CNNs) have exhibited state-of-the-art performance in various audio classification tasks. However, their real-time deployment remains a challenge on resource-constrained devices like embedde…
Audio ClassificationAudio TaggingCPU