Quality Audio Prototyping: a prototype system for unified sound retrieval and procedural generation
- URL: http://arxiv.org/abs/2606.00629v1
- Date: Sat, 30 May 2026 09:05:11 GMT
- Title: Quality Audio Prototyping: a prototype system for unified sound retrieval and procedural generation
- Abstract summary: This paper introduces Quality Audio Prototyping (QuAP), a working prototype that unifies content-based audio retrieval and procedural sound generation within a single interface.<n>QuAP integrates a similarity-based retrieval engine with real-time procedural audio models, and a rule-based assistant that provides perceptually informed parameter guidance.
- Score: 14.87513083614384
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Sound design workflows frequently oscillate between time-consuming library searches and the complexity of procedural synthesis, with practitioners typically relying on disconnected tools to address each challenge separately. This paper introduces Quality Audio Prototyping (QuAP), a working prototype that unifies content-based audio retrieval and procedural sound generation within a single interface, reducing the procedural distance between a narrative concept and its sonic realisation. QuAP integrates a similarity-based retrieval engine with real-time procedural audio models, complemented by a rule-based assistant that provides perceptually informed parameter guidance, offering definitions and recommendations derived from empirical optimisation rather than requiring prior synthesis knowledge. Preliminary evaluation confirms the viability of this approach: subjective assessment demonstrated statistically significant quality improvements in five of six embedded synthesis models, and an encoder ablation study established the preferred retrieval architecture on a sound effect dataset. A user evaluation with 16 practitioners confirmed the tool's workflow utility, with all participants agreeing that the parameter assistant preserved creative agency while lowering the barrier to procedural interaction.
Related papers
- Shared Representation Learning for Reference-Guided Targeted Sound Detection [12.339689044414401]
We propose a unified encoder architecture that processes both the reference and mixture audio within a shared representation space.<n>We establish a new state-of-the-art benchmark for targeted sound detection with a segment-level F1 score of 83.15%.
arXiv Detail & Related papers (2026-03-17T18:05:21Z) - Analysis-Driven Procedural Generation of an Engine Sound Dataset with Embedded Control Annotations [0.45835414225547183]
Computational engine sound modeling is central to the automotive audio industry.<n>These applications require large volumes of standardized, clean audio recordings with precisely time-aligned operating-state annotations.<n>We present an analysis-driven framework for generating engine audio with sample-accurate control annotations.
arXiv Detail & Related papers (2026-03-08T11:05:10Z) - AudioJudge: Understanding What Works in Large Audio Model Based Speech Evaluation [55.607230723223346]
This work presents a systematic study of Large Audio Model (LAM) as a Judge, AudioJudge, investigating whether it can provide a unified evaluation framework that addresses both challenges.<n>We explore AudioJudge across audio characteristic detection tasks, including pronunciation, speaking rate, speaker identification and speech quality, and system-level human preference simulation for automated benchmarking.<n>We introduce a multi-aspect ensemble AudioJudge to enable general-purpose multi-aspect audio evaluation. This method decomposes speech assessment into specialized judges for lexical content, speech quality, and paralinguistic features, achieving up to 0.91 Spearman correlation with human preferences on
arXiv Detail & Related papers (2025-07-17T00:39:18Z) - Where are we in audio deepfake detection? A systematic analysis over generative and detection models [59.09338266364506]
SONAR is a synthetic AI-Audio Detection Framework and Benchmark.<n>It provides a comprehensive evaluation for distinguishing cutting-edge AI-synthesized auditory content.<n>It is the first framework to uniformly benchmark AI-audio detection across both traditional and foundation model-based detection systems.
arXiv Detail & Related papers (2024-10-06T01:03:42Z) - Stem-JEPA: A Joint-Embedding Predictive Architecture for Musical Stem Compatibility Estimation [3.8570045844185237]
We present Stem-JEPA, a novel Joint-Embedding Predictive Architecture (JEPA) trained on a multi-track dataset.
Our model comprises two networks: an encoder and a predictor, which are jointly trained to predict the embeddings of compatible stems.
We evaluate our model's performance on a retrieval task on the MUSDB18 dataset, testing its ability to find the missing stem from a mix.
arXiv Detail & Related papers (2024-08-05T14:34:40Z) - A General Framework for Learning Procedural Audio Models of
Environmental Sounds [7.478290484139404]
This paper introduces the Procedural (audio) Variational autoEncoder (ProVE) framework as a general approach to learning Procedural Audio PA models.
We show that ProVE models outperform both classical PA models and an adversarial-based approach in terms of sound fidelity.
arXiv Detail & Related papers (2023-03-04T12:12:26Z) - Fully Automated End-to-End Fake Audio Detection [57.78459588263812]
This paper proposes a fully automated end-toend fake audio detection method.
We first use wav2vec pre-trained model to obtain a high-level representation of the speech.
For the network structure, we use a modified version of the differentiable architecture search (DARTS) named light-DARTS.
arXiv Detail & Related papers (2022-08-20T06:46:55Z) - End-to-End Binaural Speech Synthesis [71.1869877389535]
We present an end-to-end speech synthesis system that combines a low-bitrate audio system with a powerful decoder.
We demonstrate the capability of the adversarial loss in capturing environment effects needed to create an authentic auditory scene.
arXiv Detail & Related papers (2022-07-08T05:18:36Z) - Speaker Adaption with Intuitive Prosodic Features for Statistical
Parametric Speech Synthesis [50.5027550591763]
We propose a method of speaker adaption with intuitive prosodic features for statistical parametric speech synthesis.
The intuitive prosodic features are extracted at utterance-level or speaker-level, and are further integrated into the existing speaker-encoding-based and speaker-embedding-based adaptation frameworks respectively.
arXiv Detail & Related papers (2022-03-02T09:00:31Z) - Using growth transform dynamical systems for spatio-temporal data
sonification [9.721342507747158]
Sonification, or encoding information in meaningful audio signatures, has several advantages in augmenting or replacing traditional visualization methods for human-in-the-loop decision-making.
This paper presents a novel framework for sonifying high-dimensional data using a complex growth transform dynamical system model.
Our algorithm takes as input the data and optimization parameters underlying the learning or prediction task and combines it with the psycho parameters defined by the user.
arXiv Detail & Related papers (2021-08-21T16:25:59Z) - Any-to-Many Voice Conversion with Location-Relative Sequence-to-Sequence
Modeling [61.351967629600594]
This paper proposes an any-to-many location-relative, sequence-to-sequence (seq2seq), non-parallel voice conversion approach.
In this approach, we combine a bottle-neck feature extractor (BNE) with a seq2seq synthesis module.
Objective and subjective evaluations show that the proposed any-to-many approach has superior voice conversion performance in terms of both naturalness and speaker similarity.
arXiv Detail & Related papers (2020-09-06T13:01:06Z) - Temporal-Spatial Neural Filter: Direction Informed End-to-End
Multi-channel Target Speech Separation [66.46123655365113]
Target speech separation refers to extracting the target speaker's speech from mixed signals.
Two main challenges are the complex acoustic environment and the real-time processing requirement.
We propose a temporal-spatial neural filter, which directly estimates the target speech waveform from multi-speaker mixture.
arXiv Detail & Related papers (2020-01-02T11:12:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.