Download Audio-to-Audio via Diffusion Warm Initialization In this paper, we propose diffusion warm initialization as a simple yet effective approach for a range of audio-to-audio transformation tasks. To illustrate the generality of the approach, we demonstrate its use in timbre transfer, MIDI-to-Real synthesis, and multiple audio enhancement tasks. We conduct a detailed empirical analysis on timbre transfer to investigate the role of the initialization time t_init. The effect of t_init is evaluated using pitch-based Jaccard Distance and Fréchet Audio Distance to quantify faithfulness to the input signal and alignment with the target distribution. Our results provide practical guidance for selecting t_init and show that, once properly chosen, a single pretrained diffusion model combined with warm initialization can support multiple transformation objectives without task-specific training or conditioning. Despite its simplicity, this approach already achieves competitive results when compared with more complex pipelines designed specifically for these tasks.
Download SEND: A Spatial Event Neural Detector for Intentional Object Motion in Immersive Music Mixing Deciding exactly when to move audio objects in immersive mixes is a labor-intensive artistic task. Current tools react strictly to instantaneous frequency overlaps, lacking the macroscopic awareness required for musically intentional spatial transitions. To model these decisions, we propose SEND (Spatial Event Neural Detector). Its dual-stream architecture analyzes the target track against its background context, combining a Spec-TNT backbone and a Temporal Convolutional Network (TCN) to capture hierarchical spectral features and precise rhythmic cues. Their dynamic interplay is modeled via a novel Cross-Track Gating Interaction (CTGI) mechanism.
Download Gauss Circle Lattices with Geometric Convolutions for Synthesizing High Dimensional Image-Source Room Impulse Responses The image-source model (ISM) is a widely adopted method for efficiently simulating acoustic room impulse responses (RIRs) under specular reflection assumptions. Acoustic paths between source and receiver are traced to lattice points computed from successive reflections over bounding planes of the room. Rectangular rooms bound the total number of image-sources to be polynomial in the RIR's duration or distance k equivalent, with degree equal the number of room dimensions N. Direct ISM simulations are therefore compute upper-bound by O(k^N), and consider only cases of N≤3 for tractability and real-world applications. This work proposes an alternative computational method that lowers the asymptotic compute bound to O(Nk² log k) for integer coordinates and room dimensions via reducing ISM lattice point counting to the classic Gauss circle problem (GCP). We extend the lattice counting model to frequency-dependent and reflection weighted image-sources in higher dimensions, relating solutions between successive dimensions via the convolution operator. Two constructions for realizing RIRs are presented, along with time-frequency controls, error and run-time analysis, and RIR statistics.
Download PAEDB: A Synthetic Primary-Ambient Dataset Generation Pipeline for Automatic Upmixing Using Deep Neural Networks Automatic blind upmixing aims to convert audio from a smaller channel format (e.g. mono or stereo) into a multichannel format using estimates of direct and diffuse spatial statistics within the signal. Current approaches rely on primary-ambient extraction (PAE) algorithms, which lack real-world context through limited processing windows. Deep learning music source separation (MSS) models have been applied in voice-primary-ambient extraction (VPA) upmixing systems for handling direct components, but still rely on DSP methods of surround channel generation. This work further investigates utilizing source separation within VPA upmixing, focusing specifically on the task of stereo decorrelation and ambience extraction for 5.1 surround. We also release PAEDB (Primary–Ambient Extraction Dataset), a high-quality music dataset derived from MUSDB18-HQ and MoisesDB, comprising 1,809 primary–ambient stem pairs totaling over 550 hours of audio. The performance of selected DNNs trained on PAEDB is then evaluated using signal metrics and a listening study. Our findings indicate that DNNs can effectively model the behavior of PAE algorithms, establishing PAEDB as a strong foundation for ML upmixing systems and underscoring the need for higher-quality multichannel data to advance beyond conventional methods.
Download A Production-Oriented Framework for Evaluation of SFX Generation Industrial sound design requires audio generation systems that not only produce realistic audio, but also preserve the perceptual identity of a reference, support controllable variation, and remain efficient for practical workflows. Existing evaluations are usually tied to text-to-audio (TTA), unconditional, or task-specific settings, limiting assessment for reference-guided sound effects (SFX) variation. To address this gap, we present a production-oriented evaluation framework for structured comparison of heterogeneous audio generation and editing methods. Our framework identifies nine production requirements and explicitly accounts for differences in model capabilities, enabling comparison under a common production objective. A two-stage protocol is introduced: (1) a reference-guided audio-to-audio (ATA) variation task, in which all methods are evaluated under the same ESC-50 SFX adaptation setup, and (2) capability-specific analyses of native operations such as SFX morphing, temporal and energy alignment, inpainting, and targeted editing. This framework combines objective metrics (including FAD, ImageBind-based reference alignment, and diversity across generated variants), together with a human study of perceptual identity preservation and transient diagnosis. Our study reveals complementary strengths and trade-offs across baselines for different production needs. Among the full-generation baselines evaluated under a shared ATA setting, AudioX provides the strongest overall trade-off between reference alignment and diversity while still supporting SFX morphing. Other baselines remain most suitable for specific editing operations. Our framework establishes a structured evaluation and decision protocol for reference-guided SFX variation and provides a practical basis for designing future unified industrial audio generation pipelines. Audio demos and further details can be found on the accompanying web page.
Download Sound Effects Dataset Unification With the Universal Category System Sound effects (SFX) datasets and libraries often employ distinct tagging schemes, taxonomies, and metadata structures. This creates challenges for research on SFX classification and generation because incompatible taxonomies lead to siloed datasets that might require individualized approaches, result in non-comparable outcomes, and prevent data merging strategies. We propose a modular dataset relabeling framework that adopts the Universal Category System (UCS), an industry-standard hierarchical taxonomy for sound effects, as a shared structural foundation. This open-source framework enables us (i) to convert tags of existing datasets to UCS with a rule-based multi-stage pipeline and conflict resolution to achieve high automatic conversion rates, (ii) to suggest a stratified dataset split for the new labels, and (iii) to combine multiple datasets. To showcase the practical utility, we introduce the EnvSound-UCS dataset, a publicly available unified UCS-compliant dataset of environmental sounds with 58,057 sound clips from three sources: AudioSet, FSD50K, and ESC-50.
Download Neural Morphing: Sequence-Optimized Token-Level Morphing in Neural Audio Codecs Neural audio codecs were originally developed for high-fidelity compression; however, their latent token representations and expressive decoders also constitute a powerful substrate for controllable audio transformation. This work introduces Neural Morphing, a training-free token-domain audio effect that selects residual-vector-quantized (RVQ) token grains from a user palette and decodes the edited stream through a pretrained codec. The method combines an RVQ-group transfer policy that separates coarse, middle, and fine codebook groups with a continuity-constrained sequence matcher that replaces independent greedy selection with bounded beam search. The intended output is a controlled hybrid: the source preserves rhythmic organization while the palette contributes timbral color and residual detail. We focus on the implementation and real-time behavior of a deployable VST3/AU system, including chunked rendering, palette-size scaling, and backend health checks.
Download From Arbitrary Audio to EDM: Audio-Conditioned Retrieval of Discrete Rhythm Archetypes We present a system for transforming arbitrary audio into Electronic Dance Music (EDM) drum patterns while preserving the timbral identity of the source material. A Vector Quantized Variational Autoencoder (VQ-VAE) trained on 7,999 EDM drum loops learns a discrete codebook of 256 rhythm archetypes, validated through UMAP and hierarchical clustering to exhibit semantically meaningful structure. At inference, spectral features extracted from arbitrary input audio select the nearest archetype via nearest-neighbor retrieval in a shared audio feature space. A training sample from the selected archetype is reconstructed through the VQ-VAE, and a second decoder predicts per-hit velocity dynamics. The user's sounds are then placed at the reconstructed hit positions, scaled by predicted velocity. Applied to 2,000 files from the ESC-50 environmental sound dataset, the system activates 128 of 256 codebook entries (50% coverage), demonstrating broad responsiveness to diverse non-EDM audio.
Download Compiling Differentiable Audio Graphs to Real-Time DSP Differentiable audio processors are habitually designed and optimised in machine-learning frameworks, but deploying them as real-time audio effects still often requires non-automatic implementation in a dedicated digital signal processing language. The translation is error-prone, demands an onerous verification process, and detaches research prototypes from usable production tools. That being so, we present ADAC, a compiler that lowers a trained model to a framework-agnostic intermediate representation and emits efficient FAUST code whose impulse response matches the source model to within floating-point arithmetic noise, direct paths included. The optimisation loop is made audible by replacing the model in a running plugin after each gradient step. The exported processor carries a small set of macro-controls that leave its stability intact. A stability certificate computed from the shipped parameters is checked before the plugin is built. At the demonstration, a feedback delay network is trained and exported to a working plugin.
Download Diffusion-Based Music Audio Editing System Using Differentiable Digital Signal Processing Mixture Model This paper proposes a music audio editing system that enables source-wise editing of harmonic instrument mixtures without explicit source separation. It builds on our previously proposed score-informed method for estimating source-wise synthesis parameters, i.e., time-varying controls used to synthesize each source, such as fundamental frequency and loudness. The method directly estimates these parameters from a mixture signal and the corresponding musical score in an analysis-by-synthesis framework. Using the estimated parameters, the proposed system allows users to edit individual sources by modifying note sequences and instrument types, and then re-synthesizes the edited mixture. Through demonstrations on two-instrument mixtures, we show that the system supports note-level phrasing modification and instrument conversion of selected sources.