arXiv subjects

Xavier Lizarraga-Seijas

Publications and source records attributed to Xavier Lizarraga-Seijas.

3 recordsLinked to original sources

arXiv2026

Training Music Sample Identification Models on Real Sample Pairs

Sample identification (SI) is the task of matching pairs of tracks, where one track is created by musically transforming an element of the other. In the absence of sample annotations at scale, the dominant training paradigm has depended on artificially creating sample pairs. Although a recently released dataset provides annotations of real sample pairs at scale, an effective training recipe is missing. In this work, we present SI Embeddings (SIE), an SI model that achieves state-of-the-art results on three benchmarks, including a large-scale test set. We show that the previous state of the art trained on artificial pairs generalizes only partially to real pairs, and that its training data limits its performance. We also show that real pairs do not fully account for SIE's performance: its architecture and training recipe contribute substantially. We provide the first fully supervised training recipe for real-world SI, establishing a strong foundation for future research in the field.

cs.SD↗

arXiv2026

AllMusicCaps: Album Reviews as Complementary Supervision for Music CLAP

Recent open text-audio contrastive models (CLAPs) are typically trained with LLM-generated captions derived from tag datasets or web search results, which tend to be accurate but expressively narrow. As a complementary source, we explore human-written album reviews, specifically expert reviews from AllMusic: they exist at scale and carry narrative cues, evaluative adjectives, and scene framing that other sources lack. Since raw reviews are too noisy for direct use as captions, we first build a caption corpus with 24,5346 samples via an LLM preprocessing pipeline that identifies descriptive musical quotes and rewrites them into training-ready captions. We find that album review supervision yields the largest retrieval gains on a human-written caption benchmark (Song Describer), particularly for complex queries that other existing caption datasets leave uncovered. In addition, we revisit the training recipe and show that SigReg regularization, which encourages an isotropic Gaussian distribution in the embedding space, improves MLP probing across classification tasks, as well as text-to-music retrieval. The resulting model outperforms open CLAP-style baselines on text-to-music retrieval, zero-shot classification, and most MLP probing tasks. We release the review-derived caption dataset and model weights to support future research.

cs.SD↗

arXiv2024

Evaluating Neural Networks Architectures for Spring Reverb Modelling

Reverberation is a key element in spatial audio perception, historically achieved with the use of analogue devices, such as plate and spring reverb, and in the last decades with digital signal processing techniques that have allowed different approaches for Virtual Analogue Modelling (VAM). The electromechanical functioning of the spring reverb makes it a nonlinear system that is difficult to fully emulate in the digital domain with white-box modelling techniques. In this study, we compare five different neural network architectures, including convolutional and recurrent models, to assess their effectiveness in replicating the characteristics of this audio effect. The evaluation is conducted on two datasets at sampling rates of 16 kHz and 48 kHz. This paper specifically focuses on neural audio architectures that offer parametric control, aiming to advance the boundaries of current black-box modelling techniques in the domain of spring reverberation.

cs.SD↗