Hybrid Deep Learning Model for Singing Voice Separation

Amer, Rusul; Tmeme, Ahmed Al

doi:10.13164/mendel.2021.2.044

Cited by 3 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, the deep learning approaches has been widely used to expedite Covid-19 detection and to improve accuracy in biomedical research [19]. Deep learning demonstrated a robust performance in many applications such as, medical image detection [2], data classification, image segmentation and speech recognition [8], etc. Chest radiographs are used to diagnose patients with Covid-19 infections, as the virus primarily affects the lungs.…”

Section: Related Workmentioning

confidence: 99%

The Use of an Incremental Learning Algorithm for Diagnosing COVID-19 from Chest X-ray Images

Saif¹,

Amami²,

Eleraky³

et al. 2022

mendel

View full text Add to dashboard Cite

The new Coronavirus or simply Covid-19 causes an acute deadly disease. It has spread rapidly across the world, which has caused serious consequences for health professionals and researchers. This is due to many reasons including the lack of vaccine, shortage of testing kits and resources. Therefore, the main purpose of this study is to present an inexpensive alternative diagnostic tool for the detection of Covid-19 infection by using chest radiographs and Deep Convolutional Neural Network (DCNN) technique. In this paper, we have proposed a reliable and economical solution to detect COVID-19. This will be achieved by using X-rays of patients and an Incremental-DCNN (I-DCNN) based on ResNet-101 architecture. The datasets used in this study were collected from publicly available chest radiographs on medical repositories. The proposed I-DCNN method will help in diagnosing the positive Covid-19 patient by utilising three chest X-ray imagery groups, these will be: Covid-19, viral pneumonia, and healthy cases. Furthermore, the main contribution of this paper resides on the use of incremental learning in order to accommodate the detection system. This has high computational energy requirements, time consuming challenges, while working with large-scale and regularly evolving images. The incremental learning process will allow the recognition system to learn new datasets, while keeping the convolutional layers learned previously. The overall Covid-19 detection rate obtained using the proposed I-DCNN was of 98.70\% which undeniably can contribute effectively to the detection of COVID-19 infection.

show abstract

Section: Related Workmentioning

confidence: 99%

The Use of an Incremental Learning Algorithm for Diagnosing COVID-19 from Chest X-ray Images

Saif¹,

Amami²,

Eleraky³

et al. 2022

mendel

View full text Add to dashboard Cite

show abstract

“…Early works for speech separation solely utilized the audio signal [3][4][5][6][7]. The audio only speech separation problem is inherently challenging due to its ambiguity, making it difficult to achieve satisfactory outcomes without additional information, for example, prior knowledge or certain microphone configuration.…”

Section: Introductionmentioning

confidence: 99%

Audio-Visual Source Separation Based Fusion Techniques

Mudhafar,

Al Tmeme

2024

RIA

View full text Add to dashboard Cite

A novel hybrid deep learning model for audio-visual source separation is introduced in this paper, with a specific focus on the precise isolation of a particular speaker's voice from video content. By leveraging both audio and visual characteristics, the achievement of accurate separation of the targeted speech signal is facilitated by our model. Notably, the incorporation of the speaker's facial expressions as an auxiliary cue for enhancing the extraction of their unique vocal qualities is emphasized. Proficiency in audio-visual speech separation and latent representations of distinctive speaker attributes, known as speaker embeddings, is simultaneously acquired by our model through unsupervised learning on unannotated video data. The model employed in this study is speaker-independent, wherein an initial stage of feature extraction is conducted for both audio and visual inputs prior to the subsequent deep modal analysis. The utilization of facial attribute features as an identifying code enables the identification of the speaker's frequency space or other audio properties. The model's efficacy was assessed through evaluation on the widely recognized AVspeech dataset yielding an improvement of 7.7 in terms of source-to-distortion ratio (SDR).

show abstract

“…The process of distinguishing particular audio sources from a mixture of audio signals using visual indicators as further information is known as audio-visual source separation. This method differs from conventional ones that exclusively rely on the audio stream for source separation [1][2][3][4][5]. In other words, it is a technique that utilizes both auditory and visual information to separate individual sound sources from a mixed audio signal.…”

Section: Introductionmentioning

confidence: 99%

An Overview of Audio-Visual Source Separation Using Deep Learning

Sulaiman,

Al Tmeme,

Mahdi

2023

alkej

View full text Add to dashboard Cite

In this article, the research presents a general overview of deep learning-based AVSS (audio-visual source separation) systems. AVSS has achieved exceptional results in a number of areas, including decreasing noise levels, boosting speech recognition, and improving audio quality. The advantages and disadvantages of each deep learning model are discussed throughout the research as it reviews various current experiments on AVSS. The TCD TIMIT dataset (which contains top-notch audio and video recordings created especially for speech recognition tasks) and the Voxceleb dataset (a sizable collection of brief audio-visual clips with human speech) are just a couple of the useful datasets summarized in the paper that can be used to test AVSS systems. In its basic form, this review aims to highlight the growing importance of AVSS in improving the quality of audio signals.

show abstract

Hybrid Deep Learning Model for Singing Voice Separation

Cited by 3 publications

References 20 publications

The Use of an Incremental Learning Algorithm for Diagnosing COVID-19 from Chest X-ray Images

The Use of an Incremental Learning Algorithm for Diagnosing COVID-19 from Chest X-ray Images

Audio-Visual Source Separation Based Fusion Techniques

An Overview of Audio-Visual Source Separation Using Deep Learning

Contact Info

Product

Resources

About