Papers Audio Classification
“Audio Classification” 태그가 달린 논문 413편 · 필터 해제
AUCO ResNet: an end-to-end network for Covid-19 pre-screening from cough and breath
This study presents the Auditory Cortex ResNet (AUCO ResNet), it is a biologically inspired deep neural network especially designed for sound classification and more specifically for Covid-19 recognition from audio track…
8kAudio ClassificationCOVID-19 DiagnosisData Augmentation+4CMKD: CNN/Transformer-Based Cross-Model Knowledge Distillation for Audio Classification
Audio classification is an active research area with a wide range of applications. Over the past decade, convolutional neural networks (CNNs) have been the de-facto standard building block for end-to-end audio classifica…
Audio ClassificationKnowledge DistillationNeural Architecture Search for Energy Efficient Always-on Audio Models
Mobile and edge computing devices for always-on classification tasks require energy-efficient neural network architectures. In this paper we present several changes to neural architecture searches (NAS) that improve the …
Audio ClassificationClassificationEdge-computingEfficient Neural Network+1HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection
Audio classification is an important task of mapping audio samples into their corresponding labels. Recently, the transformer model with self-attention mechanisms has been adopted in this field. However, existing audio t…
Audio ClassificationEvent DetectionGPUKeyword Spotting+2Continual Transformers: Redundancy-Free Attention for Online Inference
Transformers in their common form are inherently limited to operate on whole token sequences rather than on one token at a time. Consequently, their use during online inference on time-series data entails considerable re…
Action DetectionAudio ClassificationClassificationOnline Action Detection+2Connecting the Dots between Audio and Text without Parallel Data through Visual Knowledge Transfer
Machines that can represent and describe environmental soundscapes have practical potential, e.g., for audio tagging and captioning. Prevailing learning paradigms of audio-text connections have been relying on parallel a…
Audio ClassificationAudio TaggingRetrievalTransfer Learning+1ARSC-Net: Adventitious Respiratory Sound Classification Network Using Parallel Paths with Channel-Spatial Attention
Automatic identification of adventitious respiratory sound has still been a challenging problem in recent years. To address this challenge, we propose an adventitious respiratory sound classification network (ARSC-Net), …
Audio ClassificationClassificationSound ClassificationClassification of Long Sequential Data using Circular Dilated Convolutional Neural Networks
Classification of long sequential data is an important Machine Learning task and appears in many application scenarios. Recurrent Neural Networks, Transformers, and Convolutional Neural Networks are three major technique…
Audio ClassificationClassificationLong-range modelingPosition+2Augmented Contrastive Self-Supervised Learning for Audio Invariant Representations
Improving generalization is a major challenge in audio classification due to labeled data scarcity. Self-supervised learning (SSL) methods tackle this by leveraging unlabeled data to learn useful features for downstream …
Audio ClassificationClassificationContrastive LearningSelf-Supervised LearningConnecting the Dots between Audio and Text without Parallel Data through Visual Knowledge Transfer
Machines that can represent and describe environmental soundscapes have practical potential, e.g., for audio tagging and captioning systems. Prevailing learning paradigms have been relying on parallel audio-text data, wh…
Audio ClassificationAudio TaggingRetrievalTransfer Learning+1X-Vector based voice activity detection for multi-genre broadcast speech-to-text
Voice Activity Detection (VAD) is a fundamental preprocessing step in automatic speech recognition. This is especially true within the broadcast industry where a wide variety of audio materials and recording conditions a…
Action DetectionActivity DetectionAudio ClassificationAutomatic Speech Recognition+4Learning music audio representations via weak language supervision
Audio representations for music information retrieval are typically learned via supervised learning in a task-specific fashion. Although effective at producing state-of-the-art results, this scheme lacks flexibility with…
Audio ClassificationInformation RetrievalMusic Information RetrievalRetrievalSound-Guided Semantic Image Manipulation
The recent success of the generative model shows that leveraging the multi-modal embedding space can manipulate an image using text information. However, manipulating an image with other sources rather than text, such as…
Audio Classificationimage-classificationImage ClassificationImage Manipulation+1PolyViT: Co-training Vision Transformers on Images, Videos and Audio
Can we train a single transformer model capable of processing multiple modalities and datasets, whilst sharing almost all of its learnable parameters? We present PolyViT, a model trained on image, audio and video which a…
Audio ClassificationSelf-Supervised Audio-Visual Representation Learning with Relaxed Cross-Modal Synchronicity
We present CrissCross, a self-supervised framework for learning audio-visual representations. A novel notion is introduced in our framework whereby in addition to learning the intra-modal and standard 'synchronous' cross…
Audio ClassificationRetrievalSelf-Supervised Action RecognitionSelf-Supervised Audio Classification+4Efficient Neuromorphic Signal Processing with Loihi 2
The biologically inspired spiking neurons used in neuromorphic computing are nonlinear filters with dynamic state variables -- very different from the stateless neuron models used in deep learning. The next version of In…
Audio ClassificationOptical Flow EstimationLungAttn: advanced lung sound classification using attention mechanism with dual TQWT and triple STFT spectrogram
Objective. Auscultation of lung sound plays an important role in the early diagnosis of lung diseases. This work aims to develop an automated adventitious lung sound detection method to reduce the workload of physicians.…
Audio ClassificationLung Sound ClassificationSound ClassificationSpecificityTemporal Knowledge Distillation for On-device Audio Classification
Improving the performance of on-device audio classification models remains a challenge given the computational limits of the mobile environment. Many studies leverage knowledge distillation to boost predictive performanc…
Audio ClassificationClassificationEvent DetectionKeyword Spotting+1Defensive Tensorization
We propose defensive tensorization, an adversarial defence technique that leverages a latent high-order factorization of the network. The layers of a network are first expressed as factorized tensor layers. Tensor dropou…
Audio ClassificationClassificationimage-classificationImage ClassificationSSAST: Self-Supervised Audio Spectrogram Transformer
Recently, neural networks based purely on self-attention, such as the Vision Transformer (ViT), have been shown to outperform deep learning models constructed with convolutional neural networks (CNNs) on various vision t…
Audio ClassificationClassificationEmotion RecognitionKeyword Spotting+3