Spectral and Cepstral Audio Noise Reduction Techniques in Speech Emotion Recognition

Pohjalainen, Jouni; Ringeval, Fabien; Schuller, Björn

doi:10.1145/2964284.2967306

Cited by 34 publications

(5 citation statements)

References 29 publications

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“…The main differences between MEMS- and dynamic-based recordings can be summarized as the former having a higher degree of background noise due to the omnidirectional nature of the MEMS microphone, and a different frequency response. In our study, a slight degree of noise cancellation was applied using an algorithm based on spectral subtraction, individually learning the noise profile of each audio recording [ 65 ]. For the frequency response, a pre-emphasis procedure was carried out mimicking the declared response of the Shure WH20; the response of an omnidirectional MEMS microphone can, conversely, be well approximated to being flat [ 66 ].…”

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence-Based Voice Assessment of Patients with Parkinson’s Disease Off and On Treatment: Machine vs. Deep-Learning Comparison

Costantini

Cesarini

Leo

et al. 2023

Sensors

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Parkinson’s Disease (PD) is one of the most common non-curable neurodegenerative diseases. Diagnosis is achieved clinically on the basis of different symptoms with considerable delays from the onset of neurodegenerative processes in the central nervous system. In this study, we investigated early and full-blown PD patients based on the analysis of their voice characteristics with the aid of the most commonly employed machine learning (ML) techniques. A custom dataset was made with hi-fi quality recordings of vocal tasks gathered from Italian healthy control subjects and PD patients, divided into early diagnosed, off-medication patients on the one hand, and mid-advanced patients treated with L-Dopa on the other. Following the current state-of-the-art, several ML pipelines were compared usingdifferent feature selection and classification algorithms, and deep learning was also explored with a custom CNN architecture. Results show how feature-based ML and deep learning achieve comparable results in terms of classification, with KNN, SVM and naïve Bayes classifiers performing similarly, with a slight edge for KNN. Much more evident is the predominance of CFS as the best feature selector. The selected features act as relevant vocal biomarkers capable of differentiating healthy subjects, early untreated PD patients and mid-advanced L-Dopa treated patients.

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Section: Methodsmentioning

confidence: 99%

Artificial Intelligence-Based Voice Assessment of Patients with Parkinson’s Disease Off and On Treatment: Machine vs. Deep-Learning Comparison

Costantini

Cesarini

Leo

et al. 2023

Sensors

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“…Audio emotion recognition (also called SER) detects the embedded emotions by processing and understanding speech signals [161]. Various ML-based and DL-based SER systems have been carried out on the basis of these extracted features for better analysis [162,163]. Traditional ML-based SER concentrates on the extraction of the acoustic features and the selection of the classifiers.…”

Section: Audio Emotion Recognitionmentioning

confidence: 99%

A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances

Wang¹,

Song²,

Tao³

et al. 2022

Preprint

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Affective computing conjoins the research topics of emotion recognition and sentiment analysis, and can be realized with unimodal or multimodal data, consisting primarily of physical information (e.g., text, audio, and visual) and physiological signals (e.g., EEG and ECG). Physical-based affect recognition caters to more researchers due to the availability of multiple public databases, but it is challenging to reveal one's inner emotion hidden purposefully from facial expressions, audio tones, body gestures, etc. Physiological signals can generate more precise and reliable emotional results; yet, the difficulty in acquiring these signals hinders their practical application. Besides, by fusing physical information and physiological signals, useful features of emotional states can be obtained to enhance the performance of affective computing models. While existing reviews focus on one specific aspect of affective computing, we provide a systematical survey of important components: emotion models, databases, and recent advances. Firstly, we introduce two typical emotion models followed by five kinds of commonly used databases for affective computing. Next, we survey and taxonomize state-of-the-art unimodal affect recognition and multimodal affective analysis in terms of their detailed architectures and performances. Finally, we discuss some critical aspects of affective computing and its applications and conclude this review by pointing out some of the most promising future directions, such as the establishment of benchmark database and fusion strategies. The overarching goal of this systematic review is to help academic and industrial researchers understand the recent advances as well as new developments in this fast-paced, high-impact domain.

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“…Critically, the accuracy will be affected by the presence of noise in the speech signal. Therefore, for reducing this noise, several noise reduction algorithms can be utilized, like minimum mean square error (MMSE) and log-spectral amplitude MMSE (LogMMSE) [35]. The crucial phases in emotion recognition are feature selection and dimension reduction.…”

Section: ) Noise Reductionmentioning

confidence: 99%

A Comprehensive Review of Speech Emotion Recognition Systems

et al. 2021

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During the last decade, Speech Emotion Recognition (SER) has emerged as an integral component within Human-computer Interaction (HCI) and other high-end speech processing systems. Generally, an SER system targets the speaker's existence of varied emotions by extracting and classifying the prominent features from a preprocessed speech signal. However, the way humans and machines recognize and correlate emotional aspects of speech signals are quite contrasting quantitatively and qualitatively, which present enormous difficulties in blending knowledge from interdisciplinary fields, particularly speech emotion recognition, applied psychology, and human-computer interface. The paper carefully identifies and synthesizes recent relevant literature related to the SER systems' varied design components/methodologies, thereby providing readers with a state-of-the-art understanding of the hot research topic. Furthermore, while scrutinizing the current state of understanding on SER systems, the research gap's prominence has been sketched out for consideration and analysis by other related researchers, institutions, and regulatory bodies.

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Spectral and Cepstral Audio Noise Reduction Techniques in Speech Emotion Recognition

Cited by 34 publications

References 29 publications

Artificial Intelligence-Based Voice Assessment of Patients with Parkinson’s Disease Off and On Treatment: Machine vs. Deep-Learning Comparison

Artificial Intelligence-Based Voice Assessment of Patients with Parkinson’s Disease Off and On Treatment: Machine vs. Deep-Learning Comparison

A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances

A Comprehensive Review of Speech Emotion Recognition Systems

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