Papers Audio Classification
“Audio Classification” 태그가 달린 논문 413편 · 필터 해제
Artificially Synthesising Data for Audio Classification and Segmentation to Improve Speech and Music Detection in Radio Broadcast
Segmenting audio into homogeneous sections such as music and speech helps us understand the content of audio. It is useful as a pre-processing step to index, store, and modify audio recordings, radio broadcasts and TV pr…
Audio ClassificationSegmentationAdaptive Weighting Scheme for Automatic Time-Series Data Augmentation
Data augmentation methods have been shown to be a fundamental technique to improve generalization in tasks such as image, text and audio classification. Recently, automated augmentation methods have led to further improv…
Audio ClassificationData Augmentationimage-classificationImage Classification+4Comparison of semi-supervised deep learning algorithms for audio classification
In this article, we adapted five recent SSL methods to the task of audio classification. The first two methods, namely Deep Co-Training (DCT) and Mean Teacher (MT), involve two collaborative neural networks. The three ot…
Audio ClassificationAudio TaggingData AugmentationDeep Learning+3Semi Supervised Learning For Few-shot Audio Classification By Episodic Triplet Mining
Few-shot learning aims to generalize unseen classes that appear during testing but are unavailable during training. Prototypical networks incorporate few-shot metric learning, by constructing a class prototype in the for…
Audio ClassificationEvent DetectionFew-Shot Audio ClassificationFew-Shot Learning+5Deep Convolutional and Recurrent Networks for Polyphonic Instrument Classification from Monophonic Raw Audio Waveforms
Sound Event Detection and Audio Classification tasks are traditionally addressed through time-frequency representations of audio signals such as spectrograms. However, the emergence of deep neural networks as efficient f…
Audio ClassificationClassificationEvent DetectionGeneral Classification+1Enhancing Audio Augmentation Methods with Consistency Learning
Data augmentation is an inexpensive way to increase training data diversity and is commonly achieved via transformations of existing data. For tasks such as classification, there is a good case for learning representatio…
Audio ClassificationAudio TaggingClassificationData Augmentation+2PSLA: Improving Audio Tagging with Pretraining, Sampling, Labeling, and Aggregation
Audio tagging is an active research area and has a wide range of applications. Since the release of AudioSet, great progress has been made in advancing model performance, which mostly comes from the development of novel …
Audio ClassificationAudio TaggingData AugmentationGeneral ClassificationLEAF: A Learnable Frontend for Audio Classification
Mel-filterbanks are fixed, engineered audio features which emulate human perception and have been used through the history of audio understanding up to today. However, their undeniable qualities are counterbalanced by th…
Audio ClassificationClassificationGeneral ClassificationPiano Skills Assessment
Can a computer determine a piano player's skill level? Is it preferable to base this assessment on visual analysis of the player's performance or should we trust our ears over our eyes? Since current CNNs have difficulty…
Action Quality AssessmentAudio ClassificationMultimodal Deep LearningSkills Assessment+3A Universal Learnable Audio Frontend
Mel-filterbanks are fixed, engineered audio features which emulate human perception and have lived through the history of audio understanding up to today. However, their undeniable qualities are counterbalanced by the fu…
Audio ClassificationA Study of Few-Shot Audio Classification
Advances in deep learning have resulted in state-of-the-art performance for many audio classification tasks but, unlike humans, these systems traditionally require large amounts of data to make accurate predictions. Not …
Audio ClassificationBIG-bench Machine LearningClassificationFew-Shot Audio Classification+3COVID-19 Cough Classification using Machine Learning and Global Smartphone Recordings
We present a machine learning based COVID-19 cough classifier which can discriminate COVID-19 positive coughs from both COVID-19 negative and healthy coughs recorded on a smartphone. This type of screening is non-contact…
Audio ClassificationBIG-bench Machine LearningCough ClassificationGeneral ClassificationZero-Shot Audio Classification with Factored Linear and Nonlinear Acoustic-Semantic Projections
In this paper, we study zero-shot learning in audio classification through factored linear and nonlinear acoustic-semantic projections between audio instances and sound classes. Zero-shot learning in audio classification…
Audio ClassificationClassificationGeneral ClassificationZero-shot Audio Classification+1Zero-Shot Audio Classification via Semantic Embeddings
In this paper, we study zero-shot learning in audio classification via semantic embeddings extracted from textual labels and sentence descriptions of sound classes. Our goal is to obtain a classifier that is capable of r…
Audio ClassificationClassificationGeneral ClassificationSentence+3RespireNet: A Deep Neural Network for Accurately Detecting Abnormal Lung Sounds in Limited Data Setting
Auscultation of respiratory sounds is the primary tool for screening and diagnosing lung diseases. Automated analysis, coupled with digital stethoscopes, can play a crucial role in enabling tele-screening of fatal lung d…
Audio ClassificationUrban Sound Classification : striving towards a fair comparison
Urban sound classification has been achieving remarkable progress and is still an active research area in audio pattern recognition. In particular, it allows to monitor the noise pollution, which becomes a growing concer…
Audio ClassificationAudio TaggingClassificationEnvironmental Sound Classification+2Multi-modal Self-Supervision from Generalized Data Transformations
In the image domain, excellent representation can be learned by inducing invariance to content-preserving transformations, such as image distortions. In this paper, we show that, for videos, the answer is more complex, a…
Audio ClassificationRetrievalvalidCRNNs for Urban Sound Tagging with spatiotemporal context
This paper describes CRNNs we used to participate in Task 5 of the DCASE 2020 challenge. This task focuses on hierarchical multilabel urban sound tagging with spatiotemporal context. The code is available on our GitHub r…
Audio ClassificationAudio TaggingEnvironmental Sound ClassificationDetecting Aedes Aegypti Mosquitoes through Audio Classification with Convolutional Neural Networks
The incidence of mosquito-borne diseases is significant in under-developed regions, mostly due to the lack of resources to implement aggressive control measurements against mosquito proliferation. A potential strategy to…
Audio ClassificationGeneral ClassificationLungRN+NL: An Improved Adventitious Lung Sound Classification Using Non-Local Block ResNet Neural Network with Mixup Data Augmentation
Performing an automated adventitious lung sound detection is a challenging task since the sound is susceptible to noises (heart-beat, motion artifacts, and audio sound) and there is subtle discrimination among different …
Audio ClassificationData AugmentationLung Sound ClassificationSound Classification