# Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations

arXiv: https://arxiv.org/abs/1909.06628 · HTML: https://mlsanity.com/paper/1909.06628/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2019.

## Built on by (most cited first, 30 of 36)

- [A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AI](https://mlsanity.com/paper/2303.13336/) (2023)
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- [NU-Wave 2: A General Neural Audio Upsampling Model for Various Sampling Rates](https://mlsanity.com/paper/2206.08545/) (2022)
- [Analysing Diffusion-based Generative Approaches versus Discriminative Approaches for Speech Restoration](https://mlsanity.com/paper/2211.02397/) (2022)
- [CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement](https://mlsanity.com/paper/2209.11112/) (2022)
- [T-FOLEY: A Controllable Waveform-Domain Diffusion Model for Temporal-Event-Guided Foley Sound Synthesis](https://mlsanity.com/paper/2401.09294/) (2024)
- [Prompt-guided Precise Audio Editing with Diffusion Models](https://mlsanity.com/paper/2406.04350/) (2024)
- [FLowHigh: Towards Efficient and High-Quality Audio Super-Resolution with Single-Step Flow Matching](https://mlsanity.com/paper/2501.04926/) (2025)
- [AERO: Audio Super Resolution in the Spectral Domain](https://mlsanity.com/paper/2211.12232/) (2022)
- [Catch-A-Waveform: Learning to Generate Audio from a Single Short Example](https://mlsanity.com/paper/2106.06426/) (2021)
- [Autoregressive Quantile Flows for Predictive Uncertainty Estimation](https://mlsanity.com/paper/2112.04643/) (2021)
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- [TRAMBA: A Hybrid Transformer and Mamba Architecture for Practical Audio and Bone Conduction Speech Super Resolution and Enhancement on Mobile and Wearable Platforms](https://mlsanity.com/paper/2405.01242/) (2024)
- [Node-wise Localization of Graph Neural Networks](https://mlsanity.com/paper/2110.14322/) (2021)
- [Self-Attention for Audio Super-Resolution](https://mlsanity.com/paper/2108.11637/) (2021)
- [Learning Continuous Representation of Audio for Arbitrary Scale Super Resolution](https://mlsanity.com/paper/2111.00195/) (2021)
- [TUNet: A Block-online Bandwidth Extension Model based on Transformers and Self-supervised Pretraining](https://mlsanity.com/paper/2110.13492/) (2021)
- [UnDiff: Unsupervised Voice Restoration with Unconditional Diffusion Model](https://mlsanity.com/paper/2306.00721/) (2023)
- [VoiceBridge: General Speech Restoration with One-step Latent Bridge Models](https://mlsanity.com/paper/2509.25275/) (2025)
- [CleanUMamba: A Compact Mamba Network for Speech Denoising using Channel Pruning](https://mlsanity.com/paper/2410.11062/) (2024)
- [Meta-learning Structure-Preserving Dynamics](https://mlsanity.com/paper/2508.11205/) (2025)
- [Fast 2DGS: Efficient Image Representation with Deep Gaussian Prior](https://mlsanity.com/paper/2512.12774/) (2025)
- [Sines, Transient, Noise Neural Modeling of Piano Notes](https://mlsanity.com/paper/2409.06513/) (2024)
- [Context-Conditioned Spatio-Temporal Predictive Learning for Reliable V2V Channel Prediction](https://mlsanity.com/paper/2409.09978/) (2024)
- [CodecFlow: Efficient Bandwidth Extension via Conditional Flow Matching in Neural Codec Latent Space](https://mlsanity.com/paper/2603.02022/) (2026)
- [Audio Spectral Enhancement: Leveraging Autoencoders for Low Latency Reconstruction of Long, Lossy Audio Sequences](https://mlsanity.com/paper/2108.03703/) (2021)
- [Speech Bandwidth Expansion Via High Fidelity Generative Adversarial Networks](https://mlsanity.com/paper/2407.18571/) (2024)
- [Modeling Time-Dependent Responses of Optical Compressors with Selective State Space Models](https://mlsanity.com/paper/2408.12549/) (2024)
- [Evaluating Neural Networks Architectures for Spring Reverb Modelling](https://mlsanity.com/paper/2409.04953/) (2024)

## Abstract (arXiv)

Learning representations that accurately capture long-range dependencies in sequential inputs -- including text, audio, and genomic data -- is a key problem in deep learning. Feed-forward convolutional models capture only feature interactions within finite receptive fields while recurrent architectures can be slow and difficult to train due to vanishing gradients. Here, we propose Temporal Feature-Wise Linear Modulation (TFiLM) -- a novel architectural component inspired by adaptive batch normalization and its extensions -- that uses a recurrent neural network to alter the activations of a convolutional model. This approach expands the receptive field of convolutional sequence models with minimal computational overhead. Empirically, we find that TFiLM significantly improves the learning speed and accuracy of feed-forward neural networks on a range of generative and discriminative learning tasks, including text classification and audio super-resolution