Pipeline for Pre-processing of Audio Data

Katti, Anantshesh; Sumana, M.

doi:10.1007/978-981-19-3575-6_21

Cited by 4 publications

(1 citation statement)

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“…In the implemented methodology, pre-processing pipeline for audio data as in [32] was implemented. The pipeline consisted of loading, padding, feature extraction, normalization of audio data and finally saving the outputs in the form of pikle extension.…”

Section: Introductionmentioning

confidence: 99%

Sentimental analysis of audio based customer reviews without textual conversion

Maradithaya,

Katti

2024

IJECE

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The current trends or procedures followed in the customer relation management system (CRM) are based on reviews, mails, and other textual data, gathered in the form of feedback from the customers. Sentiment analysis algorithms are deployed in order to gain polarity results, which can be used to improve customer services. But with evolving technologies, lately reviews or feedbacks are being dominated by audio data. As per literature, the audio contents are being translated to text and sentiments are analyzed using natural processing language techniques. However, these approaches can be time consuming. The proposed work focuses on analyzing the sentiments on the audio data itself without any textual conversion. The basic sentiment analysis polarities are mostly termed as positive, negative, and natural. But the focus is to make use of basic emotions as the base of deciding the polarity. The proposed model uses deep neural network and features such as Mel frequency cepstral coefficients (MFCC), Chroma and Mel Spectrogram on audio-based reviews.

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Section: Introductionmentioning

confidence: 99%