Center for Computer Research in Music and Acoustics
CCRMA Seeks Facilities Specialist
Happy late summer to all! The staff at CCRMA are *elated* to announce that we are searching for a new person to join our team. Please feel free to ask questions of any of us about the position.
Detailed job posting and application can be found here: https://careersearch.stanford.edu/jobs/facilities-specialist-1-on-site-2...
COVID Policies
See CCRMA's COVID policies for 2023.
Upcoming Events
Electronic Sound Poetry
FREE and Open to the Public | In Person + Livestream
Diana Deutsch on Two Perceptual Puzzles: Audio Illusions and Perfect Pitch
I'm very happy to welcome Prof. Diana Deutsch to Stanford, CCRMA and the Hearing Seminar. Diana has illustrious career at the intersection of speech and music perception. Perhaps most interestingly, how does music gets perceived as speech, and speech perceived as music. We usually think of them as different kinds of signals, perception and uses. What do these types of signals tell us about how the auditory system is organized.
Who: Dr. Diana Deutsch (CCRMA and UCSD)
What: Two Perceptual Puzzles: Audio Illusions and Perfect Pitch
Recent Events
[CANCELLED!] TEMPO VS. PITCH: UNDERSTANDING SELF-SUPERVISED TEMPO ESTIMATION
Giovana Morais (NYU) joins us to talk about her recent ICASSP paper. ABSTRACT: Self-supervision methods learn representations by solving pretext tasks that do not require human-generated labels, alleviating the need for time-consuming annotations. These methods have been applied in computer vision, natural language processing, environ- mental sound analysis, and recently in music information retrieval, e.g. for pitch estimation. Particularly in the context of music, there are few insights about the fragility of these models regarding differ- ent distributions of data, and how they could be mitigated. In this paper, we explore these questions by dissecting a self-supervised model for pitch estimation adapted for tempo estimation via rigor- ous experimentation with synthetic data.
Sound localization using a deep graph signal-processing model for acoustic imaging
ABSTRACT:
EXPLORING APPROACHES TO MULTI-TASK AUTOMATIC SYNTHESIZER PROGRAMMING
Automatic Synthesizer Programming is the task of transform-
ing an audio signal that was generated from a virtual instru-
ment, into the parameters of a sound synthesizer that would
generate this signal. In the past, this could only be done for
one virtual instrument. In this paper, we expand the current
literature by exploring approaches to automatic synthesizer
programming for multiple virtual instruments. Two different
approaches to multi-task automatic synthesizer programming
are presented. We find that the joint-decoder approach per-
forms best. We also evaluate the performance of this model
Retrieving musical information from neural data: how cognitive features enrich acoustic ones
Various features, from low-level acoustics, to higher-level
statistical regularities, to memory associations, contribute
to the experience of musical enjoyment and pleasure. Re-
cent work suggests that musical surprisal, that is, the un-
expectedness of a musical event given its context, may di-
rectly predict listeners’ experiences of pleasure and enjoy-
ment during music listening. Understanding how surprisal
shapes listeners’ preferences for certain musical pieces has
implications for music recommender systems, which are
typically content- (both acoustic or semantic) or metadata-
Recent News
Holly Herndon - Wire Cover
'Congratulations to Holly on her cover of this month's Wire! "Holly Herndon - the US sound artist and laptop auteur spreads the faith of the liberating potential of technology."
If you're a subscriber, read more here: http://www.thewire.co.uk/issues/374
CCRMA is Hiring: Temporary Position Available
Job Title: Audio Visual Engineer
Responsibilities:
