Auditory Scene Analysis: The Perceptual Organization of Sound by Albert Bregman (review)

By G. Kramer. 2016

752 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5bb86aacb5faaea4876a3241841d0c2447a3e640

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In Signal Processing

Citations in the corpus

Deep clustering: Discriminative embeddings for segmentation and separation (2015, background): “lem of scene analysis is to partition an image or video into regions attributed to the visible objects present in the scene. In audio there is a corresponding problem known as auditory scene analysis [1,2], which seeks to identify the components of audio signals corresponding to individual sound sou”
Single-Channel Multi-Speaker Separation Using Deep Clustering (2016, background): “Solving this so-called cocktail party problem [1] has proven extremely challenging for computers, and separating and recognizing speech in such conditions has been the holy grail of speech processing for more than 50 years.”
A Century of Gestalt Psychology in Visual Perception I. Perceptual Grouping and Figure-Ground Organization (2012, background): “(Hence, they also affect other sensory modalities; for a thorough discussion of grouping principles in audition, see Bregman, 1990; for a recent review of Gestalt principles in tactile perception, see Galace & Spence, 2011.)”
A real-time music-scene-description system: predominant-F0 estimation for detecting melody and bass lines in real-world audio signals (2004, methodology): “A typical research approach to computational auditory scene analysis (CASA) (Bregman, 1990; Brown, 1992; Cooke and Brown, 1993; Rosenthal and Okuno, 1995; Okuno and Cooke, 1997; 0167-6393/$ -see front matter Ó 2004 Elsevier B.V.”
Self-supervised Audio-visual Co-segmentation (2019, neutral): “ce separation. Index Terms— audio-visual, co-segmentation, disentangled, self-supervised, source separation 1. INTRODUCTION Semantic segmentation of images [2, 3] and sound source separation in audio [4, 5, 6, 7] are two important and popular tasks in the computer vision and computational audition c”
Bootstrapping Single-channel Source Separation via Unsupervised Spatial Clustering on Stereo Mixtures (2018, background): “The traditional learning procedure for these source separation models is in contrast to how humans learn to segregate audio scenes [2]: sources are rarely presented in isolation and almost never in “mixture/reference” pairs.”
The Influence of Affect on Attitude (2005, background): “Brewer, M. B. (1988). A dual process model of impression formation.”
Using uh and um in spontaneous speaking. (2002, background): “Listeners should be able to hear uh and um separately from the clauses by a perceptual process known as auditory streaming (Bregman, 1990): they would hear the primary clause as one auditory stream and uh and um as another. normally get cliticized onto the following content words, and that often…”
Objects and attention: the state of the art. (2001, methodology): “Depictions of auditory events used by Bregman (1990), Bregman and Dannenbring (1977), and Warren, Obusek, and Ackroff (1972). In each diagram, the horizontal axis represents time, while the vertical axis represents intensity.”
Object-based auditory and visual attention. (2008, background): “spectro-temporal structure, formation relies primarily on this local structure [ 12 ,13], including common onsets and offsets, harmonic structure and continuity of frequency over time.”
Perception of soundscapes: An interdisciplinary approach (2013, background): “2 suggests that a part of the relationship is the auditive figure-ground phenomenon [39]: identifiable sound sources are in the (attention) foreground and soundscape descriptors are the background.”
The development of intersensory temporal perception: an epigenetic systems/limitations view. (2000, background): “Despite structure's obvious importance, it is curious that the developmental foundations underlying the perception of temporal structure are virtually unexplored and that most theorists (Bregman, 1990; Fraisse, 1982a; Handel, 1989; Martin, 1972) simply posit an innate basis for it with very scant em”
Multitalker Speech Separation With Utterance-Level Permutation Invariant Training of Deep Recurrent Neural Networks (2017, background): “ss made in the recent years in dictating single-speaker speech [2]–[5], the progress made in multi-talker mixed speech separation and recognition, often referred to as the cocktail-party problem [6], [7], has been less impressive. Although human listeners can easily perceive separate sources in an a”
Permutation invariant training of deep models for speaker-independent multi-talker speech separation (2016, background): “Oespite the significant progress made in dietating single-speaker speech in the recent years [1, 2, 3, 4] , the progress made in multitalker mixed speech separation and recognition, often referred to as the cocktail-party problem [5, 6] , has been less impressive.”