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FIT-ASDAcad. year: 2025/2026
To introduce engineering students to the principles of audio and visual signal processing by human listeners and machines with the aim of applying this knowledge to the design of technical systems for audio signal processing. Students will become aware of the possibilities of applying knowledge of human audiovisual perception in the design of engineering systems for signal processing in artificial intelligenceState doctoral exam - topics:
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Aims
To introduce engineering students to the principles of audio and visual signal processing by human listeners and machines with the aim of applying this knowledge in the design of technical systems for audio signal processing. Students will become aware of the possibilities of applying knowledge about human audiovisual perception in the design of engineering systems for signal processing in artificial intelligence
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Syllabus
IntroductionLinking speech and hearing
Information in written and spoken languageMeasurement of informationChannel capacityTransmission of information through a communication channelInformation in printed textInformation in speech signals and in speech messages
Basic properties of hearingSimultaneous and temporal maskingCritical bands of hearingPitch perceptionTime in the perception of acoustic signalsPerception of signal modulationsPhysiology of the auditory peripheryPhysiology of higher hearing stagesFeedback and its consequences
Basic principles of speech productionLinear model of speech productionPropagation of sound in airQuarter-wave resonatorHalf-wave resonatorsConsequences of narrowing the acoustic tract (introduction of redundancy in frequency)
Speech dynamicsVocal tract movementsCorrelation between vocal tract movements and dynamics of speech envelopesSpeech modulation spectrumSpeech intelligibility with modified dynamicsCoarticulation (introduction of temporal redundancies into speech)
Short-term spectral analysisOverview of Fourier transformSampling and quantizationShort-term Fourier analysisUncertainty principle in spectral analysisCepstral analysisLinear predictive analysisApproximating the spectral envelope using LPLP spectral transformPerceptual techniques for estimating the spectral envelopeUsing spectral dynamics (RASTA filters)
Data processingLinear discriminant analysis and design of spectral projectionsLinear discriminant analysis and design of temporal RASTA filtersLinear discriminant analysis and design of 2D spectro-temporal filtersRelations between speech and hearing
History of speech recognitionNewton, Radio Rex, Spectrogram, the first recognizers and the first lessonsFeature template comparisonPrinciples of stochastic recognitionTraining and recognition using hidden Markov modelsArtificial neural networksDeriving posterior probabilities of speech sounds (DNN/HMM hybrid method)Alternative uses of artificial neural networks (TANDEM)Temporal pattern classifier (TRAPS)Current techniques
Human speech recognition by humans
Words in context and out of context (parallel context channel)Recognition of syllables filtered by high and low pass (Fletcher et al.)Recognition accuracy and articulation indexProduct of error probabilities in subbandsPossible implications in engineering
Guided consultation in combined form of studies