Search results for "audio classification"

showing 3 items of 3 documents

Open Set Audio Classification Using Autoencoders Trained on Few Data.

2020

Open-set recognition (OSR) is a challenging machine learning problem that appears when classifiers are faced with test instances from classes not seen during training. It can be summarized as the problem of correctly identifying instances from a known class (seen during training) while rejecting any unknown or unwanted samples (those belonging to unseen classes). Another problem arising in practical scenarios is few-shot learning (FSL), which appears when there is no availability of a large number of positive samples for training a recognition system. Taking these two limitations into account, a new dataset for OSR and FSL for audio data was recently released to promote research on solution…

Computer scienceOpen set02 engineering and technologylcsh:Chemical technologyMachine learningcomputer.software_genreBiochemistryArticleAnalytical ChemistrySet (abstract data type)open set recognition020204 information systemsaudio classificationautoencoders0202 electrical engineering electronic engineering information engineeringFeature (machine learning)lcsh:TP1-1185few-shot learningElectrical and Electronic EngineeringRepresentation (mathematics)Instrumentationbusiness.industryopen set classificationPerceptronClass (biology)AutoencoderAtomic and Molecular Physics and OpticsEmbedding020201 artificial intelligence & image processingArtificial intelligenceTransfer of learningbusinesscomputerSensors (Basel, Switzerland)
researchProduct

A Comparative Analysis of Residual Block Alternatives for End-to-End Audio Classification

2020

Residual learning is known for being a learning framework that facilitates the training of very deep neural networks. Residual blocks or units are made up of a set of stacked layers, where the inputs are added back to their outputs with the aim of creating identity mappings. In practice, such identity mappings are accomplished by means of the so-called skip or shortcut connections. However, multiple implementation alternatives arise with respect to where such skip connections are applied within the set of stacked layers making up a residual block. While residual networks for image classification using convolutional neural networks (CNNs) have been widely discussed in the literature, their a…

Normalization (statistics)General Computer ScienceComputer scienceFeature extractionESC02 engineering and technologycomputer.software_genreResidualConvolutional neural networkconvolutional neural networks0202 electrical engineering electronic engineering information engineeringGeneral Materials Scienceurbansound8kAudio signal processingBlock (data storage)Contextual image classificationGeneral EngineeringAudio classification020206 networking & telecommunications113 Computer and information sciences020201 artificial intelligence & image processinglcsh:Electrical engineering. Electronics. Nuclear engineeringData mininglcsh:TK1-9971computerresidual learningIEEE Access
researchProduct

Signal processing techniques for robust sound event recognition

2019

The computational analysis of acoustic scenes is today a topic of major interest, with a growing community focused on designing machines capable of identifying and understanding the sounds produced in our environment, similar to how humans perform this task. Although these domains have not reached the industrial popularity of other related audio domains, such as speech recognition or music analysis, applications designed to identify the occurrence of sounds in a given scenario are rapidly increasing. These applications are usually limited to a set of sound classes, which must be defined beforehand. In order to train sound classification models, representative sets of sound events are record…

sound event recognitionfeature selection:CIENCIAS TECNOLÓGICAS [UNESCO]audio classificationdeep learningUNESCO::CIENCIAS TECNOLÓGICASsupport vector machines
researchProduct