Speech Segregation in Background Noise Based on Deep Learning

Awotunde, Joseph Bamidele; Ogundokun, Roseline Oluwaseun; Ayo, Femi Emmanuel; Matiluko, Opeyemi Emmanuel

doi:10.1109/access.2020.3024077

Cited by 16 publications

(2 citation statements)

References 25 publications

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“…Music is a kind of art in which sound is a form of expression, which can express human emotions, regulate emotions, and promote exchange of ideas and culture, and is an indispensable part of people's daily life [1]. Harmony is a combination of multiple tones, which can effectively enhance expressive and infectious power of music, and plays an important role in musical accompaniment, including vertical structure and horizontal structure [2]. The piano is a multimusical instrument that produces harmonic effects by pressing several keys at the same time, and because of its wide range and many playing techniques, piano is known as "king of musical instruments" and occupies an important position in music performance [3].…”

Section: Introductionmentioning

confidence: 99%

Automatic Piano Harmony Arrangement System Based on Deep Learning

2022

Journal of Sensors

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Harmony, which plays an important role in enriching melody expression, is a combination of multiple notes. Melody coordination involves adding harmony effect to a single note of melody, which involves professional knowledge of basic music theory and harmony rules, and requires a high technical threshold. Under the macro background of deep learning and neural network technology, artificial intelligence is widely used in music retrieval, music creation, and music teaching. In this article, we provide a powerful tool for piano music creation by manually arranging melody and harmony instead of using deep learning. In this paper, harmonic elimination is divided into three subtasks: note detection, measurement, and multifundamental frequency estimation and model training. The music signal is divided into several segments by note detection, and the main notes and harmonic components of each segment are extracted by multifundamental frequency estimation, which are used as the features and labels of the neural network, so as to give a model with the ability of arrangement and harmony.

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Section: Introductionmentioning

confidence: 99%

Automatic Piano Harmony Arrangement System Based on Deep Learning

2022

Journal of Sensors

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“…Overall, the proposed approach shows promise for improving the performance of SER systems by leveraging both labelled and unlabelled data. Joseph Bamidele Awotunde et al [23] propose a method for enhancing speech understanding in noisy environments using speech segregation via a convolutional neural network (CNN). Their findings indicate that the proposed method outperforms some contemporary speech processing techniques by 78.52% in specific input SNR (Signal-to-Noise Ratio) settings.…”

Section: Related Workmentioning

confidence: 99%

A Survey on Speech Emotion Recognition System using CNN Algorithm

et al. 2024

IJFMR

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The speech emotion recognition system (SER) plays an important role in decoding and predicting the speaker's emotional state by analyzing audio signals. Emotions are often simplified and grouped into categories such as anger, happiness, sadness, and even neutral emotional states. These emotions serve to communicate one's thoughts and provide insight into one's physical and mental health. Speech is the most basic and natural form of communication when interacting with others. Voice signals enable rapid communication between individuals, making them a valuable and effective method of expression. Over the past decades, countless research initiatives have been devoted to the development of voice-based automatic emotion recognition systems, especially to improve human-machine communication. Voice is gradually taking center stage in the field of Human-Machine interfaces in the IT sector. This interdisciplinary field draws on a variety of fields, including computer science, signal processing, psychology, linguistics, and more. As technology advances, it allows for seamless communication between humans and machines. Speech recognition not only interprets speech but also captures nuances in a person's tone and expressions, similar to body language. Therefore, it becomes an essential element of the Human-Machine communication system.

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