COMPUTER VISION LECTURE RECORDING SOFTWARE

Source separation: methods of grouping of simultaneous sounds, such as multiple pitch detection and time-frequency clustering methods.Sequence modeling: matching and alignment between signals and note sequences.

Sound similarity: methods for comparison between sounds, sound identification, novelty detection, segmentation, and clustering.Musical knowledge structures: analysis of tonality, rhythm, and harmonies.Feature extraction: sound descriptors, segmentation, onset, pitch and envelope detection, chroma, and auditory representations.This aspect deals with time-frequency representations, both in terms of notes and spectral models, including pattern playback and audio texture. The study of CA could be roughly divided into the following sub-problems: Computational models of music and sound perception and cognition can lead to a more meaningful representation, a more intuitive digital manipulation and generation of sound and music in musical human-machine interfaces. Finally, structuring and coding the content of an audio file (sound and metadata) could benefit from efficient compression schemes, which discard inaudible information in the sound. For best results, these transformations require perceptual understanding of spectral models, high-level feature extraction, and sound analysis/synthesis. Furthermore, audio transformations such as pitch shifting, time stretching, and sound object filtering, should be perceptually and musically meaningful. Analyzing audio accurately involves several fields: electrical engineering (spectrum analysis, filtering, and audio transforms) artificial intelligence (machine learning and sound classification) psychoacoustics (sound perception) cognitive sciences (neuroscience and artificial intelligence) acoustics (physics of sound production) and music (harmony, rhythm, and timbre). In other words, to perform on par with humans, the computer should hear and understand audio content much as humans do.

COMPUTER VISION LECTURE RECORDING SOFTWARE

Since audio signals are interpreted by the human ear–brain system, that complex perceptual mechanism should be simulated somehow in software for 'machine listening'.

Machine musicianship: audition driven interactive music systems.

Computer music: use of computers in creative musical applications.

Computational musicology and mathematical music theory: use of algorithms that employ musical knowledge for analysis of music data.

Auditory scene analysis: understanding and description of audio sources and events.

Music information retrieval: methods for search and analysis of similarity between music signals.

It also differs from problems of speech understanding by machine since it deals with general audio signals, such as natural sounds and musical recordings.Īpplications of computer audition are widely varying, and include search for sounds, genre recognition, acoustic monitoring, music transcription, score following, audio texture, music improvisation, emotion in audio and so on.Ĭomputer Audition overlaps with the following disciplines: Like computer vision versus image processing, computer audition versus audio engineering deals with understanding of audio rather than processing. Technically this requires a combination of methods from the fields of signal processing, auditory modelling, music perception and cognition, pattern recognition, and machine learning, as well as more traditional methods of artificial intelligence for musical knowledge representation. Inspired by models of human audition, CA deals with questions of representation, transduction, grouping, use of musical knowledge and general sound semantics for the purpose of performing intelligent operations on audio and music signals by the computer. The engineer Paris Smaragdis, interviewed in Technology Review, talks about these systems - 'software that uses sound to locate people moving through rooms, monitor machinery for impending breakdowns, or activate traffic cameras to record accidents.' Since the notion of what it means for a machine to 'hear' is very broad and somewhat vague, computer audition attempts to bring together several disciplines that originally dealt with specific problems or had a concrete application in mind. Computer audition (CA) or machine listening is the general field of study of algorithms and systems for audio interpretation by machines.

Computer vision lecture recording