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VocalTrax recovers interpretable articulatory controls from a recording by gradient descent through a differentiable Pink Trombone synthesizer. Its vocal tract is a Kelly-Lochbaum waveguide updated twice per audio sample, so every gradient step backpropagates through a long recurrence, which is slow on GPUs. We propose Convolutional VocalTrax, which computes the exact frequency response of the same waveguide and applies it as a causal convolution. We assume that the tract is stationary within 5.8 ms blocks and that its lossy impulse response can be truncated after 93 ms. Gradients then pass through FFTs and a short recursion along the tract instead of through time. On 600 AudioMNIST recordings, the tract forward and backward pass runs 47 to 63 times faster on an A100 GPU, and a classifier trained only on real speech recognizes our reconstructions more often than those of VocalTrax (14.5% vs. 10.0%), although the log-mel distance to the target is 11% higher. Parameters fitted with our model keep this gain when rendered by the original VocalTrax (13.8%).

Automatic setlist identification aims to identify the songs performed in live concert recordings by matching noisy and variable-quality audio against a database of reference tracks. This task presents challenges beyond conventional music version identification, including substantial arrangement differences, background noise, and the need to process continuous recordings efficiently at large scale. In this work, we evaluate modern music version retrieval methods for setlist identification using the ASID benchmark, comparing the established Re-MOVE system with CLEWS and NAVE, an internal neural audio version embedding model. Our results show that NAVE and CLEWS substantially outperform Re-MOVE. Notably, NAVE uses 256-dimensional embeddings, compared with 1024 dimensions for CLEWS, providing a four-fold reduction in embedding storage while achieving better retrieval performance. We further evaluate music/non-music gating and find modest improvements in detection and temporal coverage, while qualitative analysis suggests that such preprocessing can improve recognition boundaries beyond what the standard ASID metrics capture. Overall, the results demonstrate that segment-level audio embeddings provide a strong basis for scalable setlist identification and that compact representations can achieve strong accuracy under realistic live-concert conditions at much lower storage and serving cost.

Automatic Music Transcription (AMT) relies on paired audio--MIDI datasets, implicitly assuming near-perfect temporal alignment. However, widely used piano datasets have been shown to contain alignment errors that may bias model evaluation and training. We propose a framework for evaluating audio--MIDI alignment scoring methods through controlled temporal, pitch, and structural MIDI corruptions. We also introduce STI, a scoring method built on AMT models, and benchmark it against existing alignment scoring approaches. Our results show that STI, together with methods based on CENS and spectrograms, achieves the strongest overall performance, with STI exhibiting the highest sensitivity to fine-grained temporal misalignments, the main alignment error in AMT datasets. We then apply the best-performing methods to widely used piano AMT datasets and identify trends in their relative alignment quality. These results support more informed dataset curation, model training, and AMT evaluation.

Streaming speech recognizers output a word only after the speaker has finished it, typically 90-400 ms later. For a voice agent every millisecond counts: the sooner the words are outputted, the sooner the agent can start a search or plan its reply. We present Earlybird, a 29M-parameter streaming recognizer (16.8 MB at 4 bits). Earlybird learns from audio cut off at random points, including partway through a word, and is asked to predict every word that is due by then in full, plus a guess of the next two words. Its raw token probabilities give a confidence for every word including the one still being spoken. On clean audiobook speech (LibriSpeech test-clean), the median word is predicted correctly 90 ms before the speaker finishes it, while released streaming models get it 90 to 400 ms after it ends. Earlybird is the earliest on all six test sets we time, covering audiobooks, TED talks, public speech, earnings calls, and synthetic speech. On the same audiobooks, 69% of words are predicted correctly before they end, against 3-25% for the other models. For words still being spoken, confidence is calibrated, with an expected calibration error (ECE) of 3.7 to 5.5. On the eight English Open ASR Leaderboard sets, it is more accurate than Moonshine Tiny on every set and roughly ties Whistle, a model of the same size.

Audio foundation models like CLAP, MuQ, and MERT excel at capturing general musical similarity. On the other hand, their entangled latent spaces limit interpretability and fine-grained attribute control. Conditional Similarity Networks (CSN) address this by pairing a backbone representation model with learned masks that gate the embedding space into attribute-specific subspaces. However, existing methods train the backbone from scratch, rather than leveraging pretrained foundation models. We instead present a systematic study of post-hoc disentanglement, adapting CSN to gate frozen pretrained embeddings. We evaluate adding trainable parameters to adapt the embedding under different masking and pooling regimes across two datasets. Our findings show that minimal adaptation capacity is sufficient for post-hoc gating to improve both attribute-specific disentanglement and overall retrieval performance, while pooling, masking, and depth choices yield limited additional gains.