Non-linear frequency warping using constant-Q transformation for speech emotion recognition

• URL: http://arxiv.org/abs/2102.04029v1
• Date: Mon, 8 Feb 2021 06:57:16 GMT
• Title: Non-linear frequency warping using constant-Q transformation for speech emotion recognition
• Abstract summary: We explore the constant-Q transform (CQT) for speech emotion recognition (SER) CQT-based time-frequency analysis provides variable spectro-temporal resolution with higher frequency resolution at lower frequencies. We present a comparative analysis of short-term acoustic features based on STFT and CQT for SER with deep neural network (DNN) as a back-end classifier.
• Score: 10.560561286520045
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we explore the constant-Q transform (CQT) for speech emotion recognition (SER). The CQT-based time-frequency analysis provides variable spectro-temporal resolution with higher frequency resolution at lower frequencies. Since lower-frequency regions of speech signal contain more emotion-related information than higher-frequency regions, the increased low-frequency resolution of CQT makes it more promising for SER than standard short-time Fourier transform (STFT). We present a comparative analysis of short-term acoustic features based on STFT and CQT for SER with deep neural network (DNN) as a back-end classifier. We optimize different parameters for both features. The CQT-based features outperform the STFT-based spectral features for SER experiments. Further experiments with cross-corpora evaluation demonstrate that the CQT-based systems provide better generalization with out-of-domain training data.

Related papers

• Adaptive Local Frequency Filtering for Fourier-Encoded Implicit Neural Representations [7.7579389797716365]
  We propose an adaptive local frequency filtering method for Fourier-encoded INRs.<n>The proposed method introduces a spatially varying parameter $(mathbfx)$ to modulate encoded Fourier components.<n>Experiments on 2D image fitting, 3D shape representation, and sparse data reconstruction demonstrate that the proposed method consistently improves reconstruction quality.
  arXiv  Detail & Related papers  (2026-04-03T08:04:30Z)
• Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting [25.304812011127257]
  We introduce a novel method for integrating time-frequency features through Fourier basis expansion and mapping in the time-frequency space.<n>Our approach extracts explicit frequency features while preserving temporal characteristics.<n>The results are validated on diverse real-world datasets for both long-term and short-term forecasting tasks.
  arXiv  Detail & Related papers  (2025-07-13T01:45:27Z)
• Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons [69.73249913506042]
  This paper investigates a wireless split computing architecture that employs resonate-and-fire (RF) neurons to process time-domain signals directly.<n>By resonating at tunable frequencies, RF neurons extract time-localized spectral features while maintaining low spiking activity.<n> Experimental results show that the proposed RF-SNN architecture achieves comparable accuracy to conventional LIF-SNNs and ANNs.
  arXiv  Detail & Related papers  (2025-06-24T21:14:59Z)
• A Wavelet-based Stereo Matching Framework for Solving Frequency Convergence Inconsistency [9.668149257194887]
  We propose a wavelet-based stereo matching framework (Wavelet-Stereo) for solving frequency convergence inconsistency.<n>By processing high and low frequency components separately, our framework can simultaneously refine high-frequency information in edges and low-frequency information in smooth regions.
  arXiv  Detail & Related papers  (2025-05-23T15:28:03Z)
• Robustifying Fourier Features Embeddings for Implicit Neural Representations [25.725097757343367]
  Implicit Neural Representations (INRs) employ neural networks to represent continuous functions by mapping coordinates to the corresponding values of the target function. INRs face a challenge known as spectral bias when dealing with scenes containing varying frequencies. We propose the use of multi-layer perceptrons (MLPs) without additive.
  arXiv  Detail & Related papers  (2025-02-08T07:43:37Z)
• Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Learning [81.98675881423131]
  This research addresses the challenge of developing a universal deepfake detector that can effectively identify unseen deepfake images. Existing frequency-based paradigms have relied on frequency-level artifacts introduced during the up-sampling in GAN pipelines to detect forgeries. We introduce a novel frequency-aware approach called FreqNet, centered around frequency domain learning, specifically designed to enhance the generalizability of deepfake detectors.
  arXiv  Detail & Related papers  (2024-03-12T01:28:00Z)
• Federated Quantum Long Short-term Memory (FedQLSTM) [58.50321380769256]
  Quantum federated learning (QFL) can facilitate collaborative learning across multiple clients using quantum machine learning (QML) models. No prior work has focused on developing a QFL framework that utilizes temporal data to approximate functions. A novel QFL framework that is the first to integrate quantum long short-term memory (QLSTM) models with temporal data is proposed.
  arXiv  Detail & Related papers  (2023-12-21T21:40:47Z)
• Diagnostic Spatio-temporal Transformer with Faithful Encoding [54.02712048973161]
  This paper addresses the task of anomaly diagnosis when the underlying data generation process has a complex-temporal (ST) dependency. We formalize the problem as supervised dependency discovery, where the ST dependency is learned as a side product of time-series classification. We show that temporal positional encoding used in existing ST transformer works has a serious limitation capturing frequencies in higher frequencies (short time scales) We also propose a new ST dependency discovery framework, which can provide readily consumable diagnostic information in both spatial and temporal directions.
  arXiv  Detail & Related papers  (2023-05-26T05:31:23Z)
• Vector Quantized Time Series Generation with a Bidirectional Prior Model [0.3867363075280544]
  Time series generation (TSG) studies have mainly focused on the use of Generative Adversarial Networks (GANs) combined with recurrent neural network (RNN) variants. We propose TimeVQVAE, the first work, to our knowledge, that uses vector quantization (VQ) techniques to address the TSG problem. We also propose VQ modeling in a time-frequency domain, separated into low-frequency (LF) and high-frequency (HF)
  arXiv  Detail & Related papers  (2023-03-08T17:27:39Z)
• Speech Emotion Recognition via an Attentive Time-Frequency Neural Network [21.87456615530486]
  We propose an attentive time-frequency neural network (ATFNN) for speech emotion recognition (SER) Specifically, we design a TFNN with a frequency-domain encoder (F-Encoder) based on the Transformer encoder and a time-domain encoder (T-Encoder) based on the Bidirectional Long Short-Term Memory (Bi-LSTM) To handle the second issue, we also adopt time-frequency attention with a frequency-attention network (F-Attention) and a time-attention network (T-Attention) to focus on the emotion-related frequency band ranges and time frame ranges.
  arXiv  Detail & Related papers  (2022-10-22T12:18:26Z)
• SpecGrad: Diffusion Probabilistic Model based Neural Vocoder with Adaptive Noise Spectral Shaping [51.698273019061645]
  SpecGrad adapts the diffusion noise so that its time-varying spectral envelope becomes close to the conditioning log-mel spectrogram. It is processed in the time-frequency domain to keep the computational cost almost the same as the conventional DDPM-based neural vocoders.
  arXiv  Detail & Related papers  (2022-03-31T02:08:27Z)
• Functional Regularization for Reinforcement Learning via Learned Fourier Features [98.90474131452588]
  We propose a simple architecture for deep reinforcement learning by embedding inputs into a learned Fourier basis. We show that it improves the sample efficiency of both state-based and image-based RL.
  arXiv  Detail & Related papers  (2021-12-06T18:59:52Z)
• Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains [69.62456877209304]
  We show that passing input points through a simple Fourier feature mapping enables a multilayer perceptron to learn high-frequency functions. Results shed light on advances in computer vision and graphics that achieve state-of-the-art results.
  arXiv  Detail & Related papers  (2020-06-18T17:59:11Z)
• Multi-Time-Scale Convolution for Emotion Recognition from Speech Audio Signals [7.219077740523682]
  We introduce the multi-time-scale (MTS) method to create flexibility towards temporal variations when analyzing audio data. We evaluate MTS and standard convolutional layers in different architectures for emotion recognition from speech audio, using 4 datasets of different sizes.
  arXiv  Detail & Related papers  (2020-03-06T12:28:04Z)

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.