Auditory scene analysis organizes sound events into perceptual streams

Evidence

Auditory scene analysis (ASA) was presented in Section 11.7.2. Computational auditory scene analysis (CASA) is defined by Wang and Brown (2006) as ‘the field of computational study that aims to achieve human performance in ASA by using one or two microphone recordings of the acoustic scene.’ Note that the definition does not say that the human auditory system should be imitated in the processing, only that the performance of the system should match that of a human. Human performance seems difficult to achieve, as we are very good at analysing complex acoustic scenes containing sound from multiple sources through reverberant rooms. The current CASA methods hardly achieve human performance levels in such tasks. — Ville Pulkki, Matti Karjalainen, Communication Acoustics- An…, 2574 (#expands, via linked claim)

Categorization

• XREF: connects to: Forward temporal structure is essential to auditory scene analysis.; Computational auditory scene analysis targets human-level performance without imitating the ear — Ville Pulkki, Matti Karjalainen, Communication Acoustics- An…, 2574; Within-stream pattern comparisons are easier than between-stream comparisons in auditory streaming (+6 more)

Related

• Computational auditory scene analysis targets human-level performance without imitating the ear (similarity 0.90)
• Scene analysis principles apply to cross-modal audiovisual speech mixtures. (similarity 0.88)
• The brain organizes simultaneous sound events into separate auditory streams via auditory scene analysis. (similarity 0.87)
• Auditory streaming explains how listeners separate loudspeaker from room sound (similarity 0.83)
• Speech perception begins with an audiovisual scene analysis stage (similarity 0.82)
• Sound events arranged in time and frequency are perceptually organized into streams. (similarity 0.81)
• Forward temporal structure is essential to auditory scene analysis. (similarity 0.81)
• Auditory perception runs parallel bottom-up and top-down processes (similarity 0.81)
• CASA aims to extract all perceivable information from ear canal signals. (similarity 0.81)
• Auditory scene analysis segregates sound first and forms percepts later (similarity 0.80)
• The prime auditory problem is separating and identifying sounds from an unlabeled mixture (similarity 0.80)
• The human auditory system evolved specifically for spoken-language analysis (similarity 0.80)
• Hearing acts as a spectral analyser extracting quantifiable perceptual metrics from sound (similarity 0.79)
• Visual streams can drive auditory segregation, but grouping order remains contested (similarity 0.79)
• Auditory models link acoustic stimulus features to perceived attributes computationally. (similarity 0.79)

Broader Connections

• Computational auditory scene analysis targets human-level performance without imitating the ear #expands (0.95) — Ville Pulkki, Matti Karjalainen, Communication Acoustics- An…, 2574
• Auditory streaming explains how listeners separate loudspeaker from room sound #exemplifies (0.95)
• Within-stream pattern comparisons are easier than between-stream comparisons in auditory streaming #expands (0.90)
• Perceptual segregation of target sounds may arise from temporal coherence with cortical oscillations #expands (0.85) — Dijk, Pim van, Physiology, Psychoacoustics and…, 4768
• Auditory filters are analyzed one component at a time to build up complex effects #expands (0.80)
• Scene analysis principles apply to cross-modal audiovisual speech mixtures. #expands (0.75)
• Audiovisual context coherence strengthens perceptual binding of speech syllables #expands (0.75)