Download Design of FPGA-based High-order FDTD Method for Room Acoustics Sound field rendering with finite difference time domain (FDTD) method is computation-intensive and memory-intensive. This research investigates an FPGA-based acceleration system for sound field rendering with the high-order FDTD method, in which spatial and temporal blockings are applied to alleviate external memory bandwidth bottleneck and reuse data, respectively. After implemented by using the FPGA card DE10-Pro, the FPGA-based sound field rendering systems outperform the software simulations conducted on a desktop machine with 512 GB DRAMs and a Xeon Gold 6212U processor (24 cores) running at 2.4 GHz by 11 times, 13 times, and 18 times in computing performance in the case of the 2nd-order, 4th-order, and 6th-order FDTD schemes, respectively, even though the FPGA-based sound field rendering systems run at much lower clock frequency and have much smaller on-chip and external memory.
Download Enhancing Automatic Chord Recognition via Pseudo-Labeling and Knowledge Distillation Automatic Chord Recognition (ACR) is constrained by the scarcity of aligned chord annotations, which are costly to acquire. At the same time, open-weight pre-trained models are more accessible than their proprietary training data. In this work, we present a two-stage training pipeline that leverages pre-trained models together with unlabeled audio. The proposed method decouples training into two stages. In the first stage, we use the pre-trained BTC model as a teacher to generate pseudo-labels for over 1,000 hours of diverse unlabeled audio and train a student model solely on these pseudo-labels. In the second stage, the student is continually trained on ground-truth labels as they become available. To prevent catastrophic forgetting of the representations learned in the first stage, we apply selective knowledge distillation (KD) from the teacher as a regularizer. In our experiments, two models (BTC, 2E1D) were used as students. In Stage 1, using only pseudo-labels, the BTC student achieves about 99% of the teacher's performance, while the 2E1D model achieves about 97% of the teacher's performance across seven standard mir_eval metrics. After continual training with labeled data in Stage 2, the resulting BTC student model consistently surpasses both the traditional supervised learning baseline and the original pre-trained teacher model across all metrics. The resulting 2E1D student model also outperforms the supervised baseline and approaches teacher-level performance, with both models demonstrating substantial gains on rare chord qualities.