ANIRA: An Architecture for Neural Network Inference in Real-Time Audio Applications

• URL: http://arxiv.org/abs/2506.12665v1
• Date: Sat, 14 Jun 2025 23:55:58 GMT
• Title: ANIRA: An Architecture for Neural Network Inference in Real-Time Audio Applications
• Authors: Valentin Ackva, Fares Schulz,
• Abstract summary: Anira is a cross-platform library for neural network inference.<n>OnNX, LibTorch and Lite Lite are tested for real-time audio applications.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Numerous tools for neural network inference are currently available, yet many do not meet the requirements of real-time audio applications. In response, we introduce anira, an efficient cross-platform library. To ensure compatibility with a broad range of neural network architectures and frameworks, anira supports ONNX Runtime, LibTorch, and TensorFlow Lite as backends. Each inference engine exhibits real-time violations, which anira mitigates by decoupling the inference from the audio callback to a static thread pool. The library incorporates built-in latency management and extensive benchmarking capabilities, both crucial to ensure a continuous signal flow. Three different neural network architectures for audio effect emulation are then subjected to benchmarking across various configurations. Statistical modeling is employed to identify the influence of various factors on performance. The findings indicate that for stateless models, ONNX Runtime exhibits the lowest runtimes. For stateful models, LibTorch demonstrates the fastest performance. Our results also indicate that for certain model-engine combinations, the initial inferences take longer, particularly when these inferences exhibit a higher incidence of real-time violations.

Related papers

• Designing Neural Synthesizers for Low-Latency Interaction [8.27756937768806]
  We investigate the sources of latency and jitter typically found in interactive Neural Audio Synthesis (NAS) models.<n>We then apply this analysis to the task of timbre transfer using RAVE, a convolutional variational autoencoder.<n>This culminates with a model we call BRAVE, which is low-latency and exhibits better pitch and loudness replication.
  arXiv  Detail & Related papers  (2025-03-14T16:30:31Z)
• Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity [39.483346492111515]
  Linear recurrent neural networks enable powerful long-range sequence modeling with constant memory usage and time-per-token during inference.<n>Unstructured sparsity offers a compelling solution, enabling substantial reductions in compute and memory requirements when accelerated by compatible hardware platforms.<n>We find that highly sparse linear RNNs consistently achieve better efficiency-performance trade-offs than dense baselines.
  arXiv  Detail & Related papers  (2025-02-03T13:09:21Z)
• SONNET: Enhancing Time Delay Estimation by Leveraging Simulated Audio [17.811771707446926]
  We show that learning based methods can, even based on synthetic data, significantly outperform GCC-PHAT on novel real world data. We provide our trained model, SONNET, which is runnable in real-time and works on novel data out of the box for many real data applications.
  arXiv  Detail & Related papers  (2024-11-20T10:23:21Z)
• HyperTime: Implicit Neural Representation for Time Series [131.57172578210256]
  Implicit neural representations (INRs) have recently emerged as a powerful tool that provides an accurate and resolution-independent encoding of data. In this paper, we analyze the representation of time series using INRs, comparing different activation functions in terms of reconstruction accuracy and training convergence speed. We propose a hypernetwork architecture that leverages INRs to learn a compressed latent representation of an entire time series dataset.
  arXiv  Detail & Related papers  (2022-08-11T14:05:51Z)
• MAPLE-X: Latency Prediction with Explicit Microprocessor Prior Knowledge [87.41163540910854]
  Deep neural network (DNN) latency characterization is a time-consuming process. We propose MAPLE-X which extends MAPLE by incorporating explicit prior knowledge of hardware devices and DNN architecture latency.
  arXiv  Detail & Related papers  (2022-05-25T11:08:20Z)
• A Study of Designing Compact Audio-Visual Wake Word Spotting System Based on Iterative Fine-Tuning in Neural Network Pruning [57.28467469709369]
  We investigate on designing a compact audio-visual wake word spotting (WWS) system by utilizing visual information. We introduce a neural network pruning strategy via the lottery ticket hypothesis in an iterative fine-tuning manner (LTH-IF) The proposed audio-visual system achieves significant performance improvements over the single-modality (audio-only or video-only) system under different noisy conditions.
  arXiv  Detail & Related papers  (2022-02-17T08:26:25Z)
• ANNETTE: Accurate Neural Network Execution Time Estimation with Stacked Models [56.21470608621633]
  We propose a time estimation framework to decouple the architectural search from the target hardware. The proposed methodology extracts a set of models from micro- kernel and multi-layer benchmarks and generates a stacked model for mapping and network execution time estimation. We compare estimation accuracy and fidelity of the generated mixed models, statistical models with the roofline model, and a refined roofline model for evaluation.
  arXiv  Detail & Related papers  (2021-05-07T11:39:05Z)
• Generalized Latency Performance Estimation for Once-For-All Neural Architecture Search [0.0]
  We introduce two generalizability strategies which include fine-tuning using a base model trained on a specific hardware and NAS search space. We provide a family of latency prediction models that achieve over 50% lower RMSE loss as compared to ProxylessNAS.
  arXiv  Detail & Related papers  (2021-01-04T00:48:09Z)
• LC-NAS: Latency Constrained Neural Architecture Search for Point Cloud Networks [73.78551758828294]
  LC-NAS is able to find state-of-the-art architectures for point cloud classification with minimal computational cost. We show how our searched architectures achieve any desired latency with a reasonably low drop in accuracy.
  arXiv  Detail & Related papers  (2020-08-24T10:30:21Z)
• Real Time Speech Enhancement in the Waveform Domain [99.02180506016721]
  We present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is capable of removing various kinds of background noise including stationary and non-stationary noises.
  arXiv  Detail & Related papers  (2020-06-23T09:19:13Z)

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.