发帖
雷达卡
摘要翻译：
本文提出了一个能够从音频中提取帧估计和基于音符估计的复调基音跟踪系统。该系统采用多个人工神经网络进行深度分层学习。首先，将级联网络应用于频谱图，进行帧基频(f0)估计。一个稀疏的感受野被第一个网络学习，然后作为一个过滤核用于整个系统的参数共享。f0激活跨时间连接以提取基音轮廓。这些轮廓定义了一个框架，在该框架内，后续网络执行起始和偏移检测，同时跨越时间和较小的基音波动操作。作为输入，网络使用例如来自f0估计网络的潜在表示的变化。最后，在一个迭代过程中一个接一个地删除不正确的试探性注释，从而允许网络在准确的上下文中对注释进行分类。该系统在四个公共测试集上进行了评估:MAPS、Bach10、TRIOS和MIREX Woodwind五重奏，并对所有四个数据集执行了最先进的结果。它在所有子任务中都表现良好:f0、倾斜起始和倾斜偏移跟踪。
---
英文标题：
《Polyphonic Pitch Tracking with Deep Layered Learning》
---
作者：
Anders Elowsson
---
最新提交年份：
2019
---
分类信息：
一级分类：Computer Science 计算机科学
二级分类：Sound 声音
分类描述：Covers all aspects of computing with sound, and sound as an information channel. Includes models of sound, analysis and synthesis, audio user interfaces, sonification of data, computer music, and sound signal processing. Includes ACM Subject Class H.5.5, and intersects with H.1.2, H.5.1, H.5.2, I.2.7, I.5.4, I.6.3, J.5, K.4.2.
涵盖了声音计算的各个方面，以及声音作为一种信息通道。包括声音模型、分析和合成、音频用户界面、数据的可听化、计算机音乐和声音信号处理。包括ACM学科类H.5.5，并与H.1.2、H.5.1、H.5.2、I.2.7、I.5.4、I.6.3、J.5、K.4.2交叉。
--
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Audio and Speech Processing 音频和语音处理
分类描述：Theory and methods for processing signals representing audio, speech, and language, and their applications. This includes analysis, synthesis, enhancement, transformation, classification and interpretation of such signals as well as the design, development, and evaluation of associated signal processing systems. Machine learning and pattern analysis applied to any of the above areas is also welcome. Specific topics of interest include: auditory modeling and hearing aids; acoustic beamforming and source localization; classification of acoustic scenes; speaker separation; active noise control and echo cancellation; enhancement; de-reverberation; bioacoustics; music signals analysis, synthesis and modification; music information retrieval; audio for multimedia and joint audio-video processing; spoken and written language modeling, segmentation, tagging, parsing, understanding, and translation; text mining; speech production, perception, and psychoacoustics; speech analysis, synthesis, and perceptual modeling and coding; robust speech recognition; speaker recognition and characterization; deep learning, online learning, and graphical models applied to speech, audio, and language signals; and implementation aspects ranging from system architecture to fast algorithms.
处理代表音频、语音和语言的信号的理论和方法及其应用。这包括分析、合成、增强、转换、分类和解释这些信号，以及相关信号处理系统的设计、开发和评估。机器学习和模式分析应用于上述任何领域也是受欢迎的。感兴趣的具体主题包括：听觉建模和助听器；声波束形成与声源定位；声场景分类；说话人分离；有源噪声控制和回声消除；增强；去混响；生物声学；音乐信号的分析、合成与修饰；音乐信息检索；多媒体音频和联合音视频处理；口语和书面语建模、切分、标注、句法分析、理解和翻译；文本挖掘；言语产生、感知和心理声学；语音分析、合成、感知建模和编码；鲁棒语音识别；说话人识别与特征描述；应用于语音、音频和语言信号的深度学习、在线学习和图形模型；以及从系统架构到快速算法的实现方面。
--
---
英文摘要：
This paper presents a polyphonic pitch tracking system able to extract both framewise and note-based estimates from audio. The system uses several artificial neural networks in a deep layered learning setup. First, cascading networks are applied to a spectrogram for framewise fundamental frequency (f0) estimation. A sparse receptive field is learned by the first network and then used as a filter kernel for parameter sharing throughout the system. The f0 activations are connected across time to extract pitch contours. These contours define a framework within which subsequent networks perform onset and offset detection, operating across both time and smaller pitch fluctuations at the same time. As input, the networks use, e.g., variations of latent representations from the f0 estimation network. Finally, incorrect tentative notes are removed one by one in an iterative procedure that allows a network to classify notes within an accurate context. The system was evaluated on four public test sets: MAPS, Bach10, TRIOS, and the MIREX Woodwind quintet, and performed state-of-the-art results for all four datasets. It performs well across all subtasks: f0, pitched onset, and pitched offset tracking.
---
PDF链接：
https://arxiv.org/pdf/1804.02918
京公网安备 11010802022788号
