MUS423 Research Seminars
The CCRMA Music 423 Research Seminar brings graduate students and supervising faculty together for planning and discussion of original research. Students and faculty meet either in small groups or individually, as appropriate for the research topics and interests of the participants. Research carried out is typically presented at the weekly CCRMA Colloquium (if it is of general interest to the CCRMA community) or at a Special DSP Seminar scheduled for that purpose. In either case, announcements appear on the CCRMA Home Page as Upcoming Events.
Recent DSP Seminars
Meta-AF: Meta-Learning for Adaptive Filters
Date:Fri, 11/18/2022 - 3:30pm - 4:20pmLocation:CCRMA Classroom [Knoll 217]Event Type:DSP SeminarAbstract: Adaptive filtering algorithms are pervasive throughout modern society and have had a significant impact on a wide variety of domains including audio processing, telecommunications, biomedical sensing, astrophysics and cosmology, seismology, and many more. Adaptive filters typically operate via specialized online, iterative optimization methods such as least-mean squares or recursive least squares and aim to process signals in unknown or nonstationary environments. Such algorithms, however, can be slow and laborious to develop, require domain expertise to create, and necessitate mathematical insight for improvement.
FREEOpen to the PublicFeedback Delay Networks for Artificial Reverberation
Date:Fri, 11/11/2022 - 12:00pm - 12:50pmLocation:ZoomEvent Type:DSP SeminarAbstract: Feedback delay networks (FDNs) are recursive filters widely used for artificial reverberation and decorrelation. While vast literature exists on a wide variety of reverb topologies, FDNs provide a unifying framework to design and analyze delay-based reverberators. This talk reviews recent advancements in the FDN theory, such as losslessness, modal and echo representations, and MIMO allpass properties and decorrelation. Many extensions to the FDN were proposed, including time-varying matrices, scattering matrices, high-order attenuation filters, directional reverberation, and coupled room reverberators.
Presentation RecordingFREEOpen to the PublicDeepAFx-ST: Style Transfer of Audio Effects with Differentiable Signal Processing
Date:Fri, 11/04/2022 - 3:30pm - 4:20pmLocation:CCRMA Classroom [Knoll 217]Event Type:DSP SeminarAbstract: We present a framework that can impose the audio effects and production style from one recording to another by example with the goal of simplifying the audio production process. We train a deep neural network to analyze an input recording and a style reference recording and predict the control parameters of audio effects used to render the output. In contrast to past work, we integrate audio effects as differentiable operators in our framework, perform backpropagation through audio effects, and optimize end-to-end using an audio-domain loss. We use a self-supervised training strategy enabling automatic control of audio effects without the use of any labeled or paired training data.
FREEOpen to the PublicTanguy Risset -- Compiling Audio DSP for FPGAs Using the Faust Programming Language and High Level Synthesis
Date:Fri, 10/28/2022 - 3:30pm - 4:20pmLocation:CCRMA Classroom [Knoll 217]Event Type:DSP SeminarAbstract: In this talk, we give a detailed presentation of Syfala (https://github.com/inria-emeraude/syfala), a new "audio DSP to FPGA" compiler based on the Faust programming language (https://faust.grame.fr/ ) and Xilinx/AMD's High level Synthesis (HLS) technology. Our open-source system compiles automatically audio DSP programs to FPGA hardware up to actual sound production (Zynq-based platforms). With this compiler, much smaller audio latency (i.e., one sample at a high sampling rate) can be achieved than with regular "software-based" digital audio systems. This presentation also introduces FPGA architecture in general as well as recent HLS technologies.
FREEOpen to the PublicAudio Understanding and Room Acoustics in the Era of AI
Date:Fri, 10/14/2022 - 3:30pm - 4:20pmLocation:CCRMA Classroom [Knoll 217]Event Type:DSP SeminarAbstract: This talk will aim to bridge the gap between signal processing and the latest machine learning research by discussing several applications in music and audio. In the first part of the talk, we will discuss how classic signal processing properties can be used to spoon-feed powerful neural architectures such as Transformers to tackle a difficult signal processing task: To do re-reverberation(system identification) at scale. This work now enables hearing music in any concert hall/virtual environment for any music. We use arbitrary audio recorded as an approximate proxy for a balloon pop, thus removing the need for them to measure room acoustics. This work has enormous applications in Virtual/Augmented Reality and the Metaverse if it happens!
FREEOpen to the PublicPython Programs and Book for building an audio coder and for deep learning for audio
Date:Fri, 10/07/2022 - 3:30pm - 4:20pmEvent Type:DSP SeminarLocation: CCRMA Classroom [Knoll 217]
FREEOpen to the PublicTransformers for Applications in Audio, Speech and Music: From Language Modeling to Understanding to Synthesis
Date:Thu, 05/19/2022 - 5:30pm - 6:30pmLocation:CCRMA Classroom [Knoll 217]Abstract: Transformers have touched many fields of research and music/audio is no different. This talk will present 3 of my papers as case studies on how we can leverage the power of Transformers in representation learning, signal processing, and clustering. First, we discuss how we're able to beat the wildly popular WaveNet architecture, proposed by Google-DeepMind for raw audio synthesis. We also show how we overcame the quadratic constraint of the Transformers by conditioning on context. Secondly, a version of Audio Transformers for large-scale audio understanding, inspired by viT, operating on raw waveforms, is presented.Event Type:DSP SeminarFREEFor CCRMA Users OnlyKleinPAT: Optimal Mode Conflation For Time-Domain Precomputation Of Acoustic Transfer - Jui-hsien Wang, Adobe Research
Date:Fri, 05/29/2020 - 5:30pm - 6:30pmLocation:ZoomEvent Type:DSP SeminarAbstract: We propose a new modal sound synthesis method that rapidly estimates all acoustic transfer fields of a linear modal vibration model, and greatly reduces preprocessing costs. Instead of performing a separate frequency-domain Helmholtz radiation analysis for each mode, our method partitions vibration modes into chords using optimal mode conflation, then performs a single time-domain wave simulation for each chord. We then perform transfer deconflation on each chord’s time-domain radiation field using a specialized QR solver, and thereby extract the frequency-domain transfer functions of each mode. The precomputed transfer functions are represented for fast far-field evaluation, e.g., using multipole expansions.
FREEOpen to the PublicComplex Nonlinearities for Audio Signal Processing
Date:Fri, 04/24/2020 - 5:30pm - 6:30pmLocation:Online Zoom MeetingEvent Type:DSP SeminarWe present an ongoing study of new and interesting nonlinear structures for audio signal processing, intended to be used for audio effects and synthesis. The broad intention is to fill the gap between simple, memoryless nonlinearities, and physics-based nonlinear models that require significant knowledge outside of DSP. We give a brief discussion of each nonlinear structure, and present a series of open-source audio plugins that implement the structures. Write-Up in Progress
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FREEOpen to the PublicDDSP: Differentiable Digital Signal Processing
Date:Tue, 02/18/2020 - 5:30pm - 7:00pmLocation:CCRMA Classroom [Knoll 217]Event Type:DSP SeminarAbstract: Classical DSP techniques have recently been overshadowed by deep learning for applications such as image recognition and audio generation. End-2-end learning has been key to this shift, enabling the optimization of high-dimensional nonlinear functions. However DSP techniques provide interpretability, modularity, and efficiency lacking from black-box deep networks. In this talk, I'll review the Differentiable Digital Signal Processing (DDSP) library, which enables direct integration of classic signal processing elements with deep learning methods.
FREEOpen to the Public
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