Adaptive Deep Learning for Real-Time Music Transcription Systems

Thesis Abstract

Real-time music transcription systems face enduring challenges in accurately segmenting audio into notes and chords under diverse musical styles, performances, and acoustic environments. The study addresses the problem of achieving robust, adaptive transcription that maintains high accuracy with minimal latency, enabling practical deployment in live performance, education, and assistive technologies. The aim is to develop an adaptive deep learning framework that dynamically adjusts to timbral and structural variations in input music, improving transcription accuracy while preserving real-time constraints. The specific objectives are (1) to design a neural architecture that integrates temporal attention, self-supervised pretraining, and online domain adaptation to handle varying instrumentations and recording conditions; (2) to formulate an adaptive learning mechanism that updates model parameters with low computational overhead during live usage; (3) to evaluate transcription performance across diverse genres and acoustic settings using standardized benchmarks; (4) to analyze the trade-offs between accuracy, latency, and computational load; and (5) to provide guidelines for deploying adaptive transcription in consumer and professional environments. The methodology adopts a mixed-methods, instrumented engineering approach. A cross-sectional dataset will be assembled from publicly available corpora (MAESTRO, MAPS, Hoteling Jazz) and proprietary live-recorded sessions totaling approximately 300 hours, encompassing piano, ensemble, and guitar timbres. The population comprises real-time transcription models and their deployment contexts, with a sample of 12 instrument-laden audio streams captured at 44.1 kHz. The research design combines iterative model development with controlled experimentation and user-centered evaluation. Data collection instruments include high-fidelity audio capture equipment, a real-time transcription evaluation suite, and a microbenchmarking toolkit to measure latency (ms), throughput (frames per second), and energy consumption. The adaptive model will be built on a Transformer-based encoder-decoder backbone with a dual-path temporal convolution module, augmented by a lightweight online adapter module and a confidence-based gating mechanism. Pretraining will employ self-supervised contrastive learning on unlabeled musical streams, followed by supervised fine-tuning on labeled transcription data. Online adaptation will utilize a meta-learning strategy (MAML-like) to enable rapid parameter updates with a constrained update budget. Validity and reliability will be ensured through cross-dataset validation, ablation studies, and repeated-measure testing. Data analysis will combine quantitative metrics—note and chord F-measure, onset F1, polyphony error rate, and latency—with qualitative error analysis. Statistical analyses will include repeated-measures ANOVA to compare performance across genres and environments, and regression analyses to link latency, model size, and transcription accuracy. A conceptual framework drawing on Information Theory and Selectional Theory will guide interpretation of how adaptive mechanisms balance information gain with processing cost. Expected findings include (i) improved transcription accuracy (note F1 improvement of 3–6 percentage points and chord F1 improvement of 2–5 points) under unseen timbres and reverberant conditions, (ii) reduced latency growth during online adaptation to under 80 ms per frame for 1024-sample frames, (iii) stable performance across genres due to effective domain adaptation, and (iv) a demonstrable trade-off curve illustrating optimal configurations among accuracy, latency, and power consumption. The study contributes to knowledge by integrating online adaptation with self-supervised pretraining for real-time music transcription, operationalizing a practical framework for adaptive transcription in dynamic acoustic contexts, and providing a reusable evaluation protocol and benchmark suite. The findings will inform the design of end-to-end transcription systems capable of learning from streaming data with bounded latency, advancing the state of knowledge in music information retrieval, human-computer interaction for music technology, and real-time audio processing. The conclusion emphasizes that adaptive deep learning can deliver robust real-time transcription without prohibitive computational costs, and recommendations include standardized evaluation protocols, guidelines for deploying adaptive transcription in hardware with constrained resources, and directions for future research in cross-modal adaptation and user-driven customization.