Latent Space Interpolation for Music Transitions
(2026) In Master's Theses in Mathematical Sciences FMSM01 20252Mathematical Statistics
- Abstract
- Music generation has progressed rapidly in recent years, but practical track-to-track transitions are still largely handled with conventional signal processing such as equal-power crossfades and manual beatmatching. This thesis evaluates whether latent-space interpolation in a pretrained neural audio codec can serve as a short, targeted generation operator for music transitions, where the goal is a coherent bridge rather than a simple overlap of two songs. Using EnCodec, bar-aligned segments are tempo-aligned (with optional pitch preservation), encoded to a latent timeline, and blended frame-wise using different interpolation schedules (linear, sigmoid/eased, and spherical linear interpolation). The resulting decoded transitions are... (More)
- Music generation has progressed rapidly in recent years, but practical track-to-track transitions are still largely handled with conventional signal processing such as equal-power crossfades and manual beatmatching. This thesis evaluates whether latent-space interpolation in a pretrained neural audio codec can serve as a short, targeted generation operator for music transitions, where the goal is a coherent bridge rather than a simple overlap of two songs. Using EnCodec, bar-aligned segments are tempo-aligned (with optional pitch preservation), encoded to a latent timeline, and blended frame-wise using different interpolation schedules (linear, sigmoid/eased, and spherical linear interpolation). The resulting decoded transitions are compared against baseline equal-power waveform crossfades built on the same alignment pipeline.
Evaluation combines objective similarity metrics, latent-space diagnostics, and listening tests. Time–frequency coherence (with cross-correlation alignment) is used to compare latent transitions to the baseline, while Principal Component Analysis trajectories, correlation structure, and per-channel frequency contribution probes are used to characterize how EnCodec represents musical structure during interpolation. In an A/B listening test (AB) across multiple transitions (N = 14), latent interpolation (spherical interpolation; SLERP) was preferred at a similar rate as the baseline, with a substantial fraction of “no preference” responses, indicating comparable perceptual quality rather than a clear winner.
Diagnostics further suggest structured latent trajectories and systematic channel behavior, and indicate that latent magnitude correlates with decoded power with a small temporal offset. Overall, latent interpolation in a neural codec provides a viable alternative transition operator and motivates future work on learned refinement, automatic transition point selection, and conditional generative inpainting over codec latents. (Less) - Popular Abstract
- Most music transitions are still made using tools such as beatmatching and crossfades. This work investigates whether a neurally trained audio codec can be used to create transitions directly in a compressed latent space, and whether the result can compete with traditional signal processing.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/student-papers/record/9225409
- author
- Rydén, Olle LU
- supervisor
-
- Ted Kronvall LU
- organization
- alternative title
- Interpolering i det Latenta Rummet för Musikövergångar
- course
- FMSM01 20252
- year
- 2026
- type
- H2 - Master's Degree (Two Years)
- subject
- publication/series
- Master's Theses in Mathematical Sciences
- report number
- LUTFMS-3551-2026
- ISSN
- 1404-6342
- other publication id
- 2026:E18
- language
- English
- id
- 9225409
- date added to LUP
- 2026-04-20 11:41:24
- date last changed
- 2026-06-04 15:43:18
@misc{9225409,
abstract = {{Music generation has progressed rapidly in recent years, but practical track-to-track transitions are still largely handled with conventional signal processing such as equal-power crossfades and manual beatmatching. This thesis evaluates whether latent-space interpolation in a pretrained neural audio codec can serve as a short, targeted generation operator for music transitions, where the goal is a coherent bridge rather than a simple overlap of two songs. Using EnCodec, bar-aligned segments are tempo-aligned (with optional pitch preservation), encoded to a latent timeline, and blended frame-wise using different interpolation schedules (linear, sigmoid/eased, and spherical linear interpolation). The resulting decoded transitions are compared against baseline equal-power waveform crossfades built on the same alignment pipeline.
Evaluation combines objective similarity metrics, latent-space diagnostics, and listening tests. Time–frequency coherence (with cross-correlation alignment) is used to compare latent transitions to the baseline, while Principal Component Analysis trajectories, correlation structure, and per-channel frequency contribution probes are used to characterize how EnCodec represents musical structure during interpolation. In an A/B listening test (AB) across multiple transitions (N = 14), latent interpolation (spherical interpolation; SLERP) was preferred at a similar rate as the baseline, with a substantial fraction of “no preference” responses, indicating comparable perceptual quality rather than a clear winner.
Diagnostics further suggest structured latent trajectories and systematic channel behavior, and indicate that latent magnitude correlates with decoded power with a small temporal offset. Overall, latent interpolation in a neural codec provides a viable alternative transition operator and motivates future work on learned refinement, automatic transition point selection, and conditional generative inpainting over codec latents.}},
author = {{Rydén, Olle}},
issn = {{1404-6342}},
language = {{eng}},
note = {{Student Paper}},
series = {{Master's Theses in Mathematical Sciences}},
title = {{Latent Space Interpolation for Music Transitions}},
year = {{2026}},
}