Search Results for author:
Found 8 papers, 6 papers with code
Arrange, Inpaint, and Refine: Steerable Long-term Music Audio Generation and Editing via Content-based Controls
We apply this method to fine-tune MusicGen, a leading autoregressive music generation model.
Content-based Controls For Music Large Language Modeling
We aim to further equip the models with direct and content-based controls on innate music languages such as pitch, chords and drum track.
Deep learning for non-parameterized MEMS structural design
With reasonable training, our deep learning neural network becomes a high-speed, high-accuracy calculator: it can identify the flexural mode frequency and the quality factor 4. 6 × 10 times and 2. 6 × 10 times faster, respectively, than conventional numerical simulation packages, with good accuracies of 98. 8 ± 1. 6% and 96. 8 ± 3. 1%, respectively.
A Unified Model for Zero-shot Music Source Separation, Transcription and Synthesis
We propose a unified model for three inter-related tasks: 1) to \textit{separate} individual sound sources from a mixed music audio, 2) to \textit{transcribe} each sound source to MIDI notes, and 3) to\textit{ synthesize} new pieces based on the timbre of separated sources.
Guided Learning Convolution System for DCASE 2019 Task 4
In this paper, we describe in detail the system we submitted to DCASE2019 task 4: sound event detection (SED) in domestic environments.
Guided learning for weakly-labeled semi-supervised sound event detection
Instead of designing a single model by considering a trade-off between the two sub-targets, we design a teacher model aiming at audio tagging to guide a student model aiming at boundary detection to learn using the unlabeled data.
Specialized Decision Surface and Disentangled Feature for Weakly-Supervised Polyphonic Sound Event Detection
In this paper, a special decision surface for the weakly-supervised sound event detection (SED) and a disentangled feature (DF) for the multi-label problem in polyphonic SED are proposed.
A Novel Fuzzy Search Approach over Encrypted Data with Improved Accuracy and Efficiency
To avoid privacy leaks, outsourced data usually is encrypted before being sent to cloud servers, which disables traditional search schemes for plain text.
