This paper investigates the effectiveness of using synthetic audio, generated from musical scores via virtual instrument software, for training automatic guitar transcription models. Collecting large annotated datasets from real performances is costly and labor-intensive. To overcome this, the use of synthetic data has been explored for developing automatic music transcription (AMT) models. We present a systematic comparison between a high-resolution AMT models trained on synthetic guitar data (SynthTab) and models trained on real data (GAPS), clarifying the effectiveness and limitations of synthetic data in AMT. Our experiments yield four insights: 1) models trained on diverse and well-augmented synthetic guitar data generalize well to real recordings, 2) pre-training with target instrument’s synthetic data is more effective than with non-target instrument’s real data, 3) even small synthetic datasets are valuable for pre-training, and 4) transcription discrepancies arise between models trained on synthetic data and real data, which can be mitigated by fine-tuning. These results demonstrate the utility of synthetic data for training AMT models when real aligned data is scarce.
Speech Activity Detection (SAD) systems often misclassify singing as speech, leading to degraded performance in applications such as dialogue enhancement and automatic speech recognition. We introduce Singing-Robust Speech Activity Detection (SR-SAD), a neural network designed to robustly detect speech in the presence of singing. Our key contributions are: i) a training strategy using controlled ratios of speech and singing samples to improve discrimination, ii) a computationally efficient model that maintains robust performance while reducing inference runtime, and iii) a new evaluation metric tailored to assess SAD robustness in mixed speech-singing scenarios. Experiments on a challenging dataset spanning multiple musical genres show that SR-SAD maintains high speech detection accuracy (AUC = 0.919) while rejecting singing. By explicitly learning to distinguish between speech and singing, SR-SAD enables more reliable SAD in mixed speech-singing scenarios.
In loudspeaker-based sound field reproduction, the perceived sound quality deteriorates significantly when listeners move outside of the sweet spot. Although a substantial increase in the number of loudspeakers enables rendering methods that can mitigate this issue, such a solution is not feasible for most real-life applications. This study aims to extend the listening area by finding a panning strategy that optimises an objective function reflecting the localisation and localisation uncertainty over a listening area. To that end we first introduce a psychoacoustic localisation model that outperforms existing models in the context of multichannel loudspeaker setups. Leveraging this model and an existing model of localisation uncertainty, we optimise inter-channel time and level differences for a stereophonic system. The outcome is a new panning approach that depends on the listening area and the most suitable trade-off between localisation and localisation uncertainty.
We introduce a region-of-interest beamforming approach for audio conferencing that addresses dynamic acoustics and multiple-speaker scenarios. The approach employs a two-stage sparse optimization to select a subset of microphones from dual circular sector arrays: first on the xz plane and then on the xy plane, balancing spatial resolution and efficiency. Using the dual circular layout, we are able to reduce response variability across azimuth and elevation angles. The proposed approach maximizes broadband directivity while ensuring a controlled level of distortion and minimal white noise gain. Compared to existing methods, the mainlobe attained by the resulting beamformer is more accurately aligned with the region of interest. It also achieves a preferable sidelobe and backlobe suppression. Finally, the proposed approach is shown to be superior considering the directivity factor and white noise gain, in particular at medium and high frequencies.
This paper investigates how inference step size in sliding window approaches affects music source separation quality. Through systematic analysis of seven model configurations across five architectures, we demonstrate that increased segment overlap consistently improves separation quality by up to 0.37 dB SDR. We identify a universal pattern where performance improves logarithmically with overlap, with an "elbow point" at 4-8 overlapping segments where efficiency begins to decrease rapidly. Our analysis reveals that: (1) state-of-the-art papers inconsistently report inference overlap settings, making fair comparisons difficult; (2) even modest overlap settings (25%) substantially improve quality through boundary artifact reduction; and (3) higher-performing models show proportionally greater improvements from increased overlap when accounting for the logarithmic nature of SDR. These findings suggest that standardized overlap reporting is essential for meaningful architectural comparisons and that differences attributed to architectural innovations may partly stem from undisclosed inference settings.
