Sound and Music Computing

doi:10.3390/books978-3-03842-908-1

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Keywords

wireless acoustic sensor; ambient assisted living; internet of things; edge computing; low cost; ESP32; music information retrieval; piano transcription; note recognition; note verification; onset detection; multi-pitch estimation; computer music languages; musical signal processing; csound; sound synthesis; object-oriented programming; C++; code re-use; opcodes; unit generators; music information retrieval; compositional modelling; pattern discovery; symbolic music representations; sound synthesis; physical modelling; aeroacoustics; sound effects; real-time; game audio; virtual reality; convolutional neural networks (CNN); auditory brain-computer interface (BCI); P300; virtual sound; electroencephalogram (EEG); pool strategies; classification; soundscape; acoustic environment; acoustic scene; ambisonics; spatial audio; Eigenmike; machine learning; dataset; recordings; sound and music computing; new musical instruments; pitch layouts; perception and action; motor learning; melodic similarity; alignment; stability; variation; bioinformatics; Spatial audio; Spatial sound reproduction; SDM; Headphone reproduction; Optimization; audio games; educational tools; audio signal processing; computer interfaces; music cognition; perception; training; language; automatic music transcription; multi-pitch detection; voice assignment; music signal analysis; music language models; polyphonic vocal music; music information retrieval; audio systems; digital signal processing; music; spectral analysis; spectrogram; wearable computing system; vibrating speaker for deaf; human–computer interaction; feature extraction; speech processing; audio object coding; sparsity; psychoacoustic model; multi-channel audio coding; singing synthesis; machine learning; deep learning; conditional generative models; autoregressive models; immersive virtual environment; music; performance; expressiveness; authenticity; emotions; social; mobile music; physical modeling; musical instrument design; intelligent music production; music information retrieval; multitrack mixing; stereo panning; audio equalisation; tempo estimation; spectral centroid; acoustic signal processing; circuit modeling; nonlinear waveshaping; antialiasing; synthesis; music; room response equalization; single-point equalization; multi-point equalization; adaptive equalization; wave domain filtering; sound field rendering; FPGA; FDTD; audio signal processing; analysis–synthesis; filter bank; time-frequency transform; frames; hearing; gammatone; equivalent rectangular bandwidth (ERB); Bark scale; Mel scale; machine-hearing; machine-learning; binaural room impulse response; spatial analysis; direction of arrival; convolution; reverberation; audio effect; live processing; morphing; cross synthesis; sonic interaction design; audio signal processing; music technology; ecosystems; motion; melody; shape; sound-tracing; multi-modality; convolutional neural networks; music classification; raw waveforms; sample-level filters; downsampling; filter visualization; transfer learning; tape music analysis; audio philology; digitized audio recordings; digital phylogeny; computational musicology; spectrogram alignment; audio forensics; n/a

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Sound and Music Computing

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June 2018

620 pages

ISBN978-3-03842-907-4 (Paperback)
ISBN978-3-03842-908-1 (PDF)

https://doi.org/10.3390/books978-3-03842-908-1

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This is a Reprint of the Special Issue Sound and Music Computing that was published in

Biology & Life Sciences

Chemistry & Materials Science

Computer Science & Mathematics

Engineering

Environmental & Earth Sciences

Physical Sciences

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