Ethan Manilow
Ethan is a Research Scientist on the Magenta Team, where he works on making machine learning systems that can listen to and understand musical audio in an effort to make tools that can better assist artists. He did his PhD at Northwestern University and earned BS in Physics and a BFA in Jazz Guitar at the University of Michigan. He currently lives in Chicago, where he spends his free time smiling at dogs and fingerpicking his acoustic guitar.
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What are dimensions of human intent, and how do writing tools shape and augment these expressions? From papyrus to auto-complete, a major turning point was when Alan Turing famously asked, “Can Machines Think?” If so, should we offload aspects of our thinking to machines, and what impact do they have in enabling the intentions we have? This paper adapts the Authorial Leverage framework, from the Intelligent Narrative Technologies literature, for evaluating recent generative model advancements. With increased widespread access to Large Language Models (LLMs), the evolution of our evaluative frameworks follow suit. To do this, we discuss previous expert studies of deep generative models for fiction writers and playwrights, and propose two future directions, (1) author-focused and (2) audience-focused, for furthering our understanding of Authorial Leverage of LLMs, particularly in the domain of comedy writing.
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The Chamber Ensemble Generator: Limitless High-Quality MIR Data via Generative Modeling
Yusong Wu
Josh Gardner
Curtis Glenn-Macway Hawthorne
arXiv (2022)
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Data is the lifeblood of modern machine learning systems, including for those in Music Information Retrieval (MIR). However, MIR has long been mired by small datasets and unreliable labels. In this work, we propose to break this bottleneck using generative models. By pipelining a generative model of notes (Coconet trained on Bach Chorales) with a structured synthesis model of chamber ensembles (MIDI-DDSP trained on URMP), we demonstrate a system capable of producing unlimited amounts of realistic chorale music with rich annotations including mixes, stems, MIDI, note-level performance attributes (staccato, vibrato, etc.), and even fine-grained synthesis parameters (pitch, amplitude, etc.). We call this system the \textbf{Chamber Ensemble Generator (CEG)}, and use it to generate a large dataset of chorales from four different chamber ensembles (CocoChorales). We demonstrate that data generated using our approach improves state-of-the-art models for music transcription and source separation, and we release both the system and the dataset as an open-source foundation for future work in the MIR community.
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MIDI-DDSP: Hierarchical modeling of music for detailed control
Yusong Wu
Yi Deng
Rigel Jacob Swavely
Kyle Kastner
TIm Cooijmans
Aaron Courville
ICLR 2022 (2022) (to appear)
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Musical expression requires control of both \textit{what} notes that are played, and \textit{how} they are performed. Conventional audio synthesizers provide detailed expressive controls, but at the cost of realism. Black-box neural audio synthesis and concatenative samplers can produce realistic audio, but have few mechanisms for control. In this work, we introduce MIDI-DDSP a hierarchical model of musical instruments that enables both realistic neural audio synthesis and detailed user control. Starting from interpretable Differentiable Digital Signal Processing (DDSP) synthesis parameters, we infer musical notes and high-level properties of their expressive performance (such as timbre, vibrato, dynamics, and articulation). This creates a 3-level hierarchy (notes, performance, synthesis) that affords individuals the option to intervene at each level, or utilize trained priors (performance given notes, synthesis given performance) for creative assistance. Through quantitative experiments and listening tests, we demonstrate that this hierarchy can reconstruct high-fidelity audio, accurately predict performance attributes for a note sequence, independently manipulate the attributes of a given performance, and as a complete system, generate realistic audio from a novel note sequence. By utilizing an interpretable hierarchy, with multiple levels of granularity, MIDI-DDSP opens the door to assistive tools to empower individuals across a diverse range of musical experience.
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Automatic Music Transcription (AMT), in particular the problem of automatically extracting notes from audio, has seen much recent progress via the training of neural network models on musical audio recordings paired with aligned ground-truth note labels. However, progress is currently limited by the difficulty of obtaining such note labels for natural audio recordings at scale. In this paper, we take advantage of the fact that for monophonic music, the transcription problem is much easier and largely solved via modern pitch-tracking methods. Specifically, we show that we are able to combine recordings of real monophonic music (and their transcriptions) into artificial and musically-incoherent mixtures, greatly increasing the scale of labeled training data. By pretraining on these mixtures, we can use a larger neural network model and significantly improve upon the state of the art in multi-instrument polyphonic transcription. We demonstrate this improvement across a variety of datasets and in a ``zero-shot'' setting where the model has not been trained on any data from the evaluation domain.
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MT3: Multi-task Multitrack Music Transcription
Josh Gardner
Curtis Glenn-Macway Hawthorne
ICLR 2022 (to appear)
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Automatic Music Transcription (AMT), inferring musical notes from raw audio, is a challenging task at the core of music understanding. Unlike Automatic Speech Recognition (ASR), which typically focuses on the words of a single speaker, AMT often requires transcribing multiple instruments simultaneously, all while preserving fine-scale pitch and timing information. Further, many AMT datasets are ``low resource'', as even expert musicians find music transcription difficult and time-consuming. Thus, prior work has focused on task-specific architectures, tailored to the individual instruments of each task. In this work, motivated by the promising results of sequence-to-sequence transfer learning for low-resource Natural Language Processing (NLP), we demonstrate that a general-purpose Transformer model can perform multi-task AMT, jointly transcribing arbitrary combinations of musical instruments across several transcription datasets. We show this unified training framework achieves high-quality transcription results across a range of datasets, dramatically improving performance for low-resource instruments (such as guitar), while preserving strong performance for abundant instruments (such as piano). Finally, by expanding the scope of AMT, we expose the need for more consistent evaluation metrics and better dataset alignment, and provide a strong baseline for this new direction of multi-task AMT.
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Sequence-to-Sequence Piano Transcription with Transformers
Curtis Glenn-Macway Hawthorne
Rigel Jacob Swavely
ISMIR (2021) (to appear)
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Automatic Music Transcription has seen significant progress in recent years by training custom deep neural networks on large datasets. However, these models have required extensive domain-specific design of network architectures, input/output representations, and complex decoding schemes. In this work, we show that equivalent performance can be achieved using a generic encoder-decoder Transformer with standard decoding methods. We demonstrate that the model can learn to translate spectrogram inputs directly to MIDI-like outputs for several transcription tasks. This sequence-to-sequence approach simplifies transcription by jointly modeling audio features and language-like output dependencies, thus removing the need for task-specific architectures. These results point toward possibilities for creating new Music Information Retrieval models by focusing on dataset creation and labeling rather than custom model design.
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