Audio signal classification using Linear Predictive Coding and Random Forests

Grama, Lacrimioara; Rusu, Corneliu

doi:10.1109/sped.2017.7990431

Cited by 18 publications

(8 citation statements)

References 24 publications

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“…Linear predictive coding (LPC) coefficients is based on modeling the audio signals as the weighted sum of past samples [13]. 26 Cepstrum coefficients are obtained as a feature.…”

Section: Methodsmentioning

confidence: 99%

Bilingual Speech Emotion Recognition Using Neural Networks: A Case Study for Turkish and English Languages

Ozsonmez

Acarman

Parlak

2021

Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation

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Emotion extraction and detection are considered as complex tasks due to the nature of data and subjects involved in the acquisition of sentiments. Speech analysis becomes a critical gateway in deep learning where the acoustic features would be trained to obtain more accurate descriptors to disentangle sentiments, customs in natural language. Speech feature extraction varies by the quality of audio records and linguistic properties. The speech nature is handled through a broad spectrum of emotions regarding the age, the gender and the social effects of subjects. Speech emotion analysis is fostered in English and German languages through multilevel corpus. The emotion features disseminate the acoustic analysis in videos or texts. In this study, we propose a multilingual analysis of emotion extraction using Turkish and English languages. MFCC (Mel-Frequency Cepstrum Coefficients), Mel Spectrogram, Linear Predictive Coding (LPC) and PLP-RASTA techniques are used to extract acoustic features. Three different data sets are analyzed using feed forward neural network hierarchy. Different emotion states such as happy, calm, sad and angry are compared in bilingual speech records. The accuracy and precision metrics are reached at level higher than 80%. Turkish language emotion classification is concluded to be more accurate regarding speech features.

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“…Linear predictive coding (LPC) coefficients is based on modeling the audio signals as the weighted sum of past samples [13]. 26 Cepstrum coefficients are obtained as a feature.…”

Section: Methodsmentioning

confidence: 99%

Bilingual Speech Emotion Recognition Using Neural Networks: A Case Study for Turkish and English Languages

Ozsonmez

Acarman

Parlak

2021

Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation

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“…The k-nearest neighbour classifier has been demonstrated in speech classification and speech emotion recognition [ 19 , 20 ]. Random forests have also been demonstrated for audio based problems including lip reading, speech emotion classification and audio signal classification [ 21 , 22 , 23 ]. Artificial neural networks (NNs) have revolutionised machine learning in the last few years, producing state-of-the-art performance in a wide range of applications including image classification [ 24 ], image segmentation [ 25 ], medical imaging [ 26 ] and temporal problems such as language translation [ 27 ], raw audio generation [ 28 ], visual speech recognition [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Methods for Automatic Silent Speech Recognition Using a Wearable Graphene Strain Gauge Sensor

Ravenscroft

Prattis

Kandukuri

et al. 2021

Sensors

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Silent speech recognition is the ability to recognise intended speech without audio information. Useful applications can be found in situations where sound waves are not produced or cannot be heard. Examples include speakers with physical voice impairments or environments in which audio transference is not reliable or secure. Developing a device which can detect non-auditory signals and map them to intended phonation could be used to develop a device to assist in such situations. In this work, we propose a graphene-based strain gauge sensor which can be worn on the throat and detect small muscle movements and vibrations. Machine learning algorithms then decode the non-audio signals and create a prediction on intended speech. The proposed strain gauge sensor is highly wearable, utilising graphene’s unique and beneficial properties including strength, flexibility and high conductivity. A highly flexible and wearable sensor able to pick up small throat movements is fabricated by screen printing graphene onto lycra fabric. A framework for interpreting this information is proposed which explores the use of several machine learning techniques to predict intended words from the signals. A dataset of 15 unique words and four movements, each with 20 repetitions, was developed and used for the training of the machine learning algorithms. The results demonstrate the ability for such sensors to be able to predict spoken words. We produced a word accuracy rate of 55% on the word dataset and 85% on the movements dataset. This work demonstrates a proof-of-concept for the viability of combining a highly wearable graphene strain gauge and machine leaning methods to automate silent speech recognition.

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“…Among possible applications are the identification of deforestation threats and illegal logging activities, through automatic detection of specific sounds like several engines, chainsaws, or vehicles. Detecting other illegal activities like hunting in forest or ecological reserves, by spotting gun shots, or human voices would be a useful application [6]- [9]. In recent times, solutions based on environmental sound recognition are applied in early wildfire detection [10].…”

Section: Introductionmentioning

confidence: 99%

Environmental Acoustics Modelling Techniques for Forest Monitoring

Segarceanu¹,

Suciu²,

Gavăt³

2021

Adv. sci. technol. eng. syst. j.

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Environmental sounds detection plays an increasing role in computer science and robotics as it simulates the human faculty of hearing. It is applied in environment research, monitoring and protection, by allowing investigation of natural reserves, and showing potential risks of damage that can be deduced from the environmental acoustic. The research presented in this paper is related to the development of an intelligent forest environment monitoring solution, which applies signal analysis algorithm to detect endangering sounds. Environmental sounds are processed using some modelling algorithms based on which the acoustic forest events can be classified into one of the categories: chainsaw, vehicle, genuine forest background noise. The article will explore and compare several methodologies for environmental sound classification, among which the dominant Deep Neural Networks, the Long Short-Term Memory, and the classical Gaussian Mixtures Modelling and Dynamic Time Warping.

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Audio signal classification using Linear Predictive Coding and Random Forests

Cited by 18 publications

References 24 publications

Bilingual Speech Emotion Recognition Using Neural Networks: A Case Study for Turkish and English Languages

Bilingual Speech Emotion Recognition Using Neural Networks: A Case Study for Turkish and English Languages

Machine Learning Methods for Automatic Silent Speech Recognition Using a Wearable Graphene Strain Gauge Sensor

Environmental Acoustics Modelling Techniques for Forest Monitoring

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