標(biāo)題: Titlebook: Advances in Speech and Music Technology; Proceedings of FRSM Anupam Biswas,Emile Wennekes,Alicja Wieczorkowska Conference proceedings 2021 [打印本頁(yè)] 作者: 有判斷力 時(shí)間: 2025-3-21 17:06
書(shū)目名稱(chēng)Advances in Speech and Music Technology影響因子(影響力)
書(shū)目名稱(chēng)Advances in Speech and Music Technology影響因子(影響力)學(xué)科排名
書(shū)目名稱(chēng)Advances in Speech and Music Technology網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱(chēng)Advances in Speech and Music Technology網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Advances in Speech and Music Technology被引頻次
書(shū)目名稱(chēng)Advances in Speech and Music Technology被引頻次學(xué)科排名
書(shū)目名稱(chēng)Advances in Speech and Music Technology年度引用
書(shū)目名稱(chēng)Advances in Speech and Music Technology年度引用學(xué)科排名
書(shū)目名稱(chēng)Advances in Speech and Music Technology讀者反饋
書(shū)目名稱(chēng)Advances in Speech and Music Technology讀者反饋學(xué)科排名
作者: Arresting 時(shí)間: 2025-3-21 21:56
Anupam Biswas,Emile Wennekes,Alicja WieczorkowskaPresents recent research in the field of speech and music processing.Discusses the outcomes of FRSM 2020, held in NIT, Silchar, India.Serves as a reference guide for researchers and practitioners in a作者: emission 時(shí)間: 2025-3-22 01:03 作者: Offensive 時(shí)間: 2025-3-22 06:29 作者: 走路左晃右晃 時(shí)間: 2025-3-22 09:28 作者: ELUDE 時(shí)間: 2025-3-22 16:51 作者: antiquated 時(shí)間: 2025-3-22 20:25
Mehdi Achour,Jamel Belhadj Tahere used to enhance their expressivity and project their voices. These singers use various adjustment of vocal fold to yield harmonically more vibrant voices. However, most of the heavy metal vocalists tend to overuse their vocal systems, which can have an adverse effect on their quality of voice. The作者: RADE 時(shí)間: 2025-3-22 21:28 作者: 寬敞 時(shí)間: 2025-3-23 03:19 作者: zonules 時(shí)間: 2025-3-23 08:10 作者: 旋轉(zhuǎn)一周 時(shí)間: 2025-3-23 13:33 作者: 出處 時(shí)間: 2025-3-23 16:20 作者: 果核 時(shí)間: 2025-3-23 20:47 作者: 進(jìn)步 時(shí)間: 2025-3-24 02:02 作者: FILTH 時(shí)間: 2025-3-24 03:12 作者: 性學(xué)院 時(shí)間: 2025-3-24 10:30 作者: Initial 時(shí)間: 2025-3-24 13:22 作者: Forehead-Lift 時(shí)間: 2025-3-24 15:18
Cognitive Packets in Large Virtual Networks taught by several musicians. Thus, this paper aims at helping them with the advanced technology we have today. In order to play a musical instrument, knowing the Shruti and Sargam (terms used to refer to tone and notes, respectively, in Indian Classical Music) of the music being played is very impo作者: HEDGE 時(shí)間: 2025-3-24 19:51 作者: Vertical 時(shí)間: 2025-3-25 01:35 作者: ANTI 時(shí)間: 2025-3-25 05:40 作者: 臭名昭著 時(shí)間: 2025-3-25 08:34 作者: AVID 時(shí)間: 2025-3-25 15:12
Style of Vocal Singers in Indian Classical Music: Timbre Approachy among partials and spectral centroids. Beside these, shimmer, jitter and frequency per partial number were also descriptive. The study concludes that timbre is an important feature to distinguish or identify the style of a vocalist.作者: 長(zhǎng)處 時(shí)間: 2025-3-25 19:17 作者: covert 時(shí)間: 2025-3-25 22:45
Tandem Networks with Intermittent Energyosed scheme offers to store low-quality audio which can generate high quality with help of a share, this comes out to be space-efficient and we also don’t need to maintain two separate copies of the same audio. In the proposed scheme, an audio quality control method has been defined that can be used in modern audio platforms.作者: 套索 時(shí)間: 2025-3-26 02:35 作者: FER 時(shí)間: 2025-3-26 06:59 作者: multiply 時(shí)間: 2025-3-26 11:59 作者: 構(gòu)成 時(shí)間: 2025-3-26 12:39 作者: inundate 時(shí)間: 2025-3-26 16:46 作者: 故意釣到白楊 時(shí)間: 2025-3-27 00:31
Vocalist Identification in Audio Songs Using Convolutional Neural Networkprocessing of dataset involves the conversion of an audio file into a spectrogram, i.e. visual representation of frequencies of audio signal as it varies with time and then uses these spectrograms as an image to train a convolutional neural network (CNN) for classification of vocalists in an audio song.作者: 神刊 時(shí)間: 2025-3-27 01:19 作者: laceration 時(shí)間: 2025-3-27 07:34 作者: PLIC 時(shí)間: 2025-3-27 11:46
Robert R. Chodorek,Agnieszka Chodoreks in real time and is successful in removing maximum noise. To be above this difficulty, this paper presents an efficient algorithm for noise detection which works on the principles of deep learning, specifically convolutional neural networks (CNNs) and the removal of similar noise from the audio using the Python module ‘noise reducer.’作者: 相容 時(shí)間: 2025-3-27 14:15 作者: 施加 時(shí)間: 2025-3-27 21:04 作者: Pastry 時(shí)間: 2025-3-28 00:01
2194-5357 as a reference guide for researchers and practitioners in a.This book features original papers from 25th?International Symposium on Frontiers of Research in Speech and Music (FRSM 2020), jointly organized by National Institute of Technology, Silchar, India, during 8–9 October 2020. The book is orga作者: 頭盔 時(shí)間: 2025-3-28 02:26
Rafa? Wo?niak,Danuta Zakrzewskaof research. In this paper, an attempt is made to give an overview of existing areas of research in music signal processing. Existing methodologies in these respective areas are explained in detail. A brief overview of future perspectives is also discussed.作者: Interim 時(shí)間: 2025-3-28 09:48 作者: ANNUL 時(shí)間: 2025-3-28 10:54
Music Signal Processing: A Literature Surveyof research. In this paper, an attempt is made to give an overview of existing areas of research in music signal processing. Existing methodologies in these respective areas are explained in detail. A brief overview of future perspectives is also discussed.作者: Coronation 時(shí)間: 2025-3-28 17:28 作者: 搖曳 時(shí)間: 2025-3-28 21:54
Conference proceedings 2021ational Institute of Technology, Silchar, India, during 8–9 October 2020. The book is organized in five sections, considering both technological advancement and interdisciplinary nature of speech and music processing. The first section contains chapters covering the foundations of both vocal and ins作者: ITCH 時(shí)間: 2025-3-29 00:07
Mehdi Achour,Jamel Belhadj Tahernd the singer’s formant were extracted and compared statistically. Results of the study revealed a higher singing power ratio for the pre-fatigue condition compared to the post-fatigue condition and the singer’s formant did not show significant differences between the pre-fatigue and post-fatigue co作者: GUMP 時(shí)間: 2025-3-29 04:52
Sangman Moh,Kyoung Park,Sungnam Kiman classical music which are fundamental in unfolding the musical ornamentation of a raga. Here, we have discussed only two ornamentations, viz. meend and andolan. The study leads to identifying the style of vocal performers.作者: Oscillate 時(shí)間: 2025-3-29 07:26
SCTP Based Framework for Mobile Web Agent and amplitude of the speech signals. Depending on these parameters, the speech signals are classified into appropriate classes of emotions through various algorithms of pattern recognition and machine learning. An algorithm for emotion detection is proposed here and is implemented in R tool. The al作者: mortuary 時(shí)間: 2025-3-29 14:11
Computer and Information Sciences IIs to identify the gender of the speaker by using highly accurate, efficient, and reliable machine learning algorithms. These two are the most important aspects of any audio surveillance system. The algorithm is tested on the Open Speech and Language Resources (Open SLR) dataset. This dataset consist作者: bleach 時(shí)間: 2025-3-29 18:58
Ata Turk,B. Barla Cambazoglu,Cevdet Aykanatis presented for this task which distinguishes the five major types of folk songs of Bengal. Experiments were performed with over 43 K clips, and the best correct classification rate of 99.73% was obtained with LPCC-based features considering extremely short length clips.作者: 愛(ài)了嗎 時(shí)間: 2025-3-29 19:59 作者: Ischemic-Stroke 時(shí)間: 2025-3-30 02:40 作者: 只有 時(shí)間: 2025-3-30 06:21 作者: –LOUS 時(shí)間: 2025-3-30 12:11
Style Identification of Vocal Singers in Indian Classical Music Using Meend and Andolanan classical music which are fundamental in unfolding the musical ornamentation of a raga. Here, we have discussed only two ornamentations, viz. meend and andolan. The study leads to identifying the style of vocal performers.作者: 斷斷續(xù)續(xù) 時(shí)間: 2025-3-30 15:37 作者: oxidant 時(shí)間: 2025-3-30 18:18
Machine Learning Approach for Audio Surveillance Using Rs to identify the gender of the speaker by using highly accurate, efficient, and reliable machine learning algorithms. These two are the most important aspects of any audio surveillance system. The algorithm is tested on the Open Speech and Language Resources (Open SLR) dataset. This dataset consist作者: 無(wú)底 時(shí)間: 2025-3-30 20:55
An Artificial Intelligence-Based Approach Towards Segregation of Folk Songsis presented for this task which distinguishes the five major types of folk songs of Bengal. Experiments were performed with over 43 K clips, and the best correct classification rate of 99.73% was obtained with LPCC-based features considering extremely short length clips.作者: 反感 時(shí)間: 2025-3-31 01:47
