Keywords : Convolution Neural Network

Research on DNN Methods in Music Source Separation Tools with emphasis to Spleeter

Louis Ansal C A; Ancy C A

International Research Journal on Advanced Science Hub, 2021, Volume 3, Issue Special Issue 6S, Pages 24-28
DOI: 10.47392/irjash.2021.160

This paper tries to attempt a review on deep neural network (DNN) method in music source separation (MSS) tools with emphasis to Spleeter by Deezer, an enhanced deep learning model for music sourceseparation. It is a set of pre-trainedmodel written in python using the Tensorflow machine learning library used for musicsource separation. It was developed by Deezer, on the need to separate a given mixed music track to its constituentinstrumental or vocal tracks usually known as stems. Spleeter offers 3 pre-trainedmodels namely 2, 4, and 5 stemseparation models that are capable of separating a given mix into 2, 4, and 5 stems respectively, which can be used forvarious needs like remixing, up-mixing,music transcription, etc. This paper is the first of its kind to review on DNN methods in MSS.In this paper, we will learn about the purpose and useof Spleeter developed by Deezer as well as about the technical aspect behind this software product that includes areas like ArtificialIntelligence (AI), Machine Learning and Deep Learning, and further about Time-Frequency (TF) masking and U-NetConvolution Neural Network (CNN) which are the methodology and architecture employed in it respectively. From thereview, we learned that Spleeter by Deezer is one of the latest advancement in MSS problem that comparatively has one of the best signal to distortion ratio (SDR), signal to artifacts ratio (SAR), signal to interference ratio (SIR), and sourceimage to spatial distortion ratio (ISR) and produce a state of the art solution, and it has also paved a way togreater development in MSS problem in the future.

Plant Disease Detection Using Deep Learning

Kowshik B; Savitha V; Nimosh madhav M; Karpagam G; Sangeetha K

International Research Journal on Advanced Science Hub, 2021, Volume 3, Issue Special Issue ICARD-2021 3S, Pages 30-33
DOI: 10.47392/irjash.2021.057

Agriculture is extremely important in human life. Almost 60% of the population is engaged in some kind of agriculture, either directly or indirectly. There are no technologies in the traditional system to detect diseases in various crops in an agricultural environment, which is why farmers are not interested in increasing their agricultural productivity day by day. Crop diseases have an impact on the growth of their respective species, so early detection is critical. Many Machine Learning (ML) models have been used to detect and classify crop diseases, but with recent advances in a subset of ML, Deep Learning (DL), this area of research appears to have a lot of promise in terms of improved accuracy. The proposed method uses a convolutional neural network and a Deep Neural Network to identify and recognise crop disease symptoms effectively and accurately. Furthermore, multiple efficiency metrics are used to assess these strategies. This article offers a thorough description of the DL models that are used to visualise crop diseases. Furthermore, several research gaps are identified from which greater transparency for detecting diseases in plants can be obtained, even before symptoms occur. The proposed methodology aims to develop a convolution neural network-based strategy for detecting plant leaf disease.