Papers Audio Classification
“Audio Classification” 태그가 달린 논문 413편 · 필터 해제
belabBERT: a Dutch RoBERTa-based language model applied to psychiatric classification
Natural language processing (NLP) is becoming an important means for automatic recognition of human traits and states, such as intoxication, presence of psychiatric disorders, presence of airway disorders and states of s…
Audio ClassificationClassificationLanguage ModelingLanguage Modelling+2Receptive Field Regularization Techniques for Audio Classification and Tagging with Deep Convolutional Neural Networks
In this paper, we study the performance of variants of well-known Convolutional Neural Network (CNN) architectures on different audio tasks. We show that tuning the Receptive Field (RF) of CNNs is crucial to their genera…
Acoustic Scene ClassificationAudio ClassificationInstrument RecognitionScene ClassificationSingle-Layer Vision Transformers for More Accurate Early Exits with Less Overhead
Deploying deep learning models in time-critical applications with limited computational resources, for instance in edge computing systems and IoT networks, is a challenging task that often relies on dynamic inference met…
Audio ClassificationCrowd CountingEdge-computingSparse Spiking Gradient Descent
There is an increasing interest in emulating Spiking Neural Networks (SNNs) on neuromorphic computing devices due to their low energy consumption. Recent advances have allowed training SNNs to a point where they start to…
Audio ClassificationImage ClassificationAccent Recognition with Hybrid Phonetic Features
The performance of voice-controlled systems is usually influenced by accented speech. To make these systems more robust, the frontend accent recognition (AR) technologies have received increased attention in recent years…
Audio ClassificationAutomatic Speech RecognitionAutomatic Speech Recognition (ASR)speech-recognition+1Audio Transformers
Over the past two decades, CNN architectures have produced compelling models of sound perception and cognition, learning hierarchical organizations of features. Analogous to successes in computer vision, audio feature cl…
Audio ClassificationUnsupervised Pre-trainingIDMT-Traffic: An Open Benchmark Dataset for Acoustic Traffic Monitoring Research
In many urban areas, traffic load and noise pollution are constantly increasing. Automated systems for traffic monitoring are promising countermeasures, which allow to systematically quantify and predict local traffic fl…
Audio ClassificationGeneral ClassificationDASEE A Synthetic Database of Domestic Acoustic Scenes and Events in Dementia Patients Environment
Access to informative databases is a crucial part of notable research developments. In the field of domestic audio classification, there have been significant advances in recent years. Although several audio databases ex…
Audio ClassificationRoom Impulse Response (RIR)VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
We present a framework for learning multimodal representations from unlabeled data using convolution-free Transformer architectures. Specifically, our Video-Audio-Text Transformer (VATT) takes raw signals as inputs and e…
Action ClassificationAction RecognitionAction Recognition In VideosAudio Classification+9Augmenting Deep Classifiers with Polynomial Neural Networks
Deep neural networks have been the driving force behind the success in classification tasks, e.g., object and audio recognition. Impressive results and generalization have been achieved by a variety of recently proposed …
Audio ClassificationGeneral ClassificationImage ClassificationModel Compression+3Audio feature ranking for sound-based COVID-19 patient detection
Audio classification using breath and cough samples has recently emerged as a low-cost, non-invasive, and accessible COVID-19 screening method. However, a comprehensive survey shows that no application has been approved …
Audio ClassificationClassificationGeneral ClassificationAST: Audio Spectrogram Transformer
In the past decade, convolutional neural networks (CNNs) have been widely adopted as the main building block for end-to-end audio classification models, which aim to learn a direct mapping from audio spectrograms to corr…
Audio ClassificationAudio TaggingClassificationGeneral Classification+3COVID-19 Detection in Cough, Breath and Speech using Deep Transfer Learning and Bottleneck Features
We present an experimental investigation into the effectiveness of transfer learning and bottleneck feature extraction in detecting COVID-19 from audio recordings of cough, breath and speech. This type of screening is no…
Audio ClassificationBIG-bench Machine LearningTransfer LearningBroaden Your Views for Self-Supervised Video Learning
Most successful self-supervised learning methods are trained to align the representations of two independent views from the data. State-of-the-art methods in video are inspired by image techniques, where these two views …
Audio ClassificationOptical Flow EstimationRepresentation LearningSelf-Supervised Action Recognition+2Self-paced ensemble learning for speech and audio classification
Combining multiple machine learning models into an ensemble is known to provide superior performance levels compared to the individual components forming the ensemble. This is because models can complement each other in …
Audio ClassificationClassificationEnsemble LearningGeneral Classification+1Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks
Inspired by more detailed modeling of biological neurons, Spiking neural networks (SNNs) have been investigated both as more biologically plausible and potentially more powerful models of neural computation, and also wit…
Audio Classificationdomain classificationGeneral ClassificationGesture RecognitionMulti-Format Contrastive Learning of Audio Representations
Recent advances suggest the advantage of multi-modal training in comparison with single-modal methods. In contrast to this view, in our work we find that similar gain can be obtained from training with different formats …
Audio ClassificationContrastive LearningSlow-Fast Auditory Streams For Audio Recognition
We propose a two-stream convolutional network for audio recognition, that operates on time-frequency spectrogram inputs. Following similar success in visual recognition, we learn Slow-Fast auditory streams with separable…
Audio ClassificationHuman Interaction RecognitionEnvironmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices
Significant efforts are being invested to bring state-of-the-art classification and recognition to edge devices with extreme resource constraints (memory, speed, and lack of GPU support). Here, we demonstrate the first d…
Audio ClassificationEnvironmental Sound ClassificationGPUModel Compression+2Perceiver: General Perception with Iterative Attention
Biological systems perceive the world by simultaneously processing high-dimensional inputs from modalities as diverse as vision, audition, touch, proprioception, etc. The perception models used in deep learning on the ot…
3D Point Cloud ClassificationAudio ClassificationImage Classification