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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.

‹ Colloquium Series up Hearing Seminars ›
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Upcoming DSP Seminars

  • Adaptive and interactive machine listening with minimal supervision

    Date: 
    Fri, 02/10/2023 - 4:30pm - 5:20pm
    Location: 
    CCRMA Classroom [Knoll 217]
    Event Type: 
    DSP Seminar
    Abstract: Nowadays deep learning-based approaches have become popular tools and achieved promising results in machine listening. However, a deep model that generalizes well needs to be trained on a large amount of labeled data. Rare, fine-grained, or newly emerged classes (e.g. a rare musical instrument or a new sound effect) where large-scale data collection is hard or simply impossible are often considered out-of-vocabulary and unsupported by machine listening systems. In this thesis work, we aim to provide new perspectives and approaches to machine listening tasks with limited labeled data. Specifically, we focus on algorithms that are designed to work with few labeled data (e.g. few-shot learning) and incorporate human input to guide the machine.
    FREE
    Open to the Public

Recent DSP Seminars

  • Meta-AF: Meta-Learning for Adaptive Filters

    Date: 
    Fri, 11/18/2022 - 3:30pm - 4:20pm
    Location: 
    CCRMA Classroom [Knoll 217]
    Event Type: 
    DSP Seminar
    Abstract: 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.
    FREE
    Open to the Public
  • Feedback Delay Networks for Artificial Reverberation

    Date: 
    Fri, 11/11/2022 - 12:00pm - 12:50pm
    Location: 
    Zoom
    Event Type: 
    DSP Seminar
    Abstract: 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 Recording
    FREE
    Open to the Public
  • DeepAFx-ST: Style Transfer of Audio Effects with Differentiable Signal Processing

    Date: 
    Fri, 11/04/2022 - 3:30pm - 4:20pm
    Location: 
    CCRMA Classroom [Knoll 217]
    Event Type: 
    DSP Seminar
    Abstract: 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.
    FREE
    Open to the Public
  • Tanguy Risset -- Compiling Audio DSP for FPGAs Using the Faust Programming Language and High Level Synthesis

    Date: 
    Fri, 10/28/2022 - 3:30pm - 4:20pm
    Location: 
    CCRMA Classroom [Knoll 217]
    Event Type: 
    DSP Seminar
    Abstract: 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.
    FREE
    Open to the Public
  • Audio Understanding and Room Acoustics in the Era of AI

    Date: 
    Fri, 10/14/2022 - 3:30pm - 4:20pm
    Location: 
    CCRMA Classroom [Knoll 217]
    Event Type: 
    DSP Seminar
    Abstract: 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!
    FREE
    Open to the Public
  • Python Programs and Book for building an audio coder and for deep learning for audio

    Date: 
    Fri, 10/07/2022 - 3:30pm - 4:20pm
    Event Type: 
    DSP Seminar
    Location: CCRMA Classroom [Knoll 217]
    FREE
    Open to the Public
  • Transformers for Applications in Audio, Speech and Music: From Language Modeling to Understanding to Synthesis

    Date: 
    Thu, 05/19/2022 - 5:30pm - 6:30pm
    Location: 
    CCRMA Classroom [Knoll 217]
    Event Type: 
    DSP Seminar
    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.
    FREE
    For CCRMA Users Only
  • KleinPAT: Optimal Mode Conflation For Time-Domain Precomputation Of Acoustic Transfer - Jui-hsien Wang, Adobe Research

    Date: 
    Fri, 05/29/2020 - 5:30pm - 6:30pm
    Location: 
    Zoom
    Event Type: 
    DSP Seminar
    Abstract: 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.
    FREE
    Open to the Public
  • Complex Nonlinearities for Audio Signal Processing

    Date: 
    Fri, 04/24/2020 - 5:30pm - 6:30pm
    Location: 
    Online Zoom Meeting
    Event Type: 
    DSP Seminar
    We 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
    To receive your Zoom meeting link via email, please RSVP
    FREE
    Open to the Public
  • DDSP: Differentiable Digital Signal Processing

    Date: 
    Tue, 02/18/2020 - 5:30pm - 7:00pm
    Location: 
    CCRMA Classroom [Knoll 217]
    Event Type: 
    DSP Seminar
    Abstract: 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.
    FREE
    Open to the Public
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