Automatic Transcription of Piano Music Using Audio-Vision Fusion

2013 ◽  
Vol 333-335 ◽  
pp. 742-748
Author(s):  
Yu Long Wan ◽  
Zhi Gang Wu ◽  
Ruo Hua Zhou ◽  
Yong Hong Yan
Keyword(s):  
High Performance ◽  
Matched Filter ◽  
Piano Music ◽  
Onset Detection ◽  
Music Transcription ◽  
Transcription System ◽  
Hands On ◽  
Automatic Transcription ◽  
Piano Transcription ◽  
Application Specific

Over the last decade many sophisticated and application-specific methods have been proposed for transcription of polyphonic music. However, the performance seems to have reached a limit. This paper describes a high-performance piano transcription system with two main contributions. Firstly, a new onset detection method is proposed using a specific energy envelope matched filter, which has been proved very suitable for piano music. Secondly, a computer-vision method is proposed to enhance audio-only piano music transcription, using the recognition of the player's hands on the piano keyboard. We carried out comparable experiments respectively for onset detection and overall system based on the MAPS database and the video database. The results were compared with the best piano transcription system in MIREX 2008, which still kept the best performance in piano subset till MIREX 2012. The results show that the system outperforms the state-of-art method substantially.

High-Performance Reconfigurable Computing

2019 ◽  
pp. 731-744
Author(s):  
Mário Pereira Vestias
Keyword(s):  
Power Consumption ◽  
Integrated Circuit ◽  
Reconfigurable Computing ◽  
High Performance ◽  
General Purpose ◽  
Reconfigurable Hardware ◽  
Coarse Grained ◽  
Lower Power ◽  
Fine Grained ◽  
Application Specific

High-performance reconfigurable computing systems integrate reconfigurable technology in the computing architecture to improve performance. Besides performance, reconfigurable hardware devices also achieve lower power consumption compared to general-purpose processors. Better performance and lower power consumption could be achieved using application-specific integrated circuit (ASIC) technology. However, ASICs are not reconfigurable, turning them application specific. Reconfigurable logic becomes a major advantage when hardware flexibility permits to speed up whatever the application with the same hardware module. The first and most common devices utilized for reconfigurable computing are fine-grained FPGAs with a large hardware flexibility. To reduce the performance and area overhead associated with the reconfigurability, coarse-grained reconfigurable solutions has been proposed as a way to achieve better performance and lower power consumption. In this chapter, the authors provide a description of reconfigurable hardware for high-performance computing.

High-Performance Reconfigurable Computing

2018 ◽  
pp. 4018-4029
Author(s):  
Mário Pereira Vestias
Keyword(s):  
Power Consumption ◽  
Integrated Circuit ◽  
Reconfigurable Computing ◽  
High Performance ◽  
General Purpose ◽  
Reconfigurable Hardware ◽  
Coarse Grained ◽  
Lower Power ◽  
Fine Grained ◽  
Application Specific

High-Performance Reconfigurable Computing systems integrate reconfigurable technology in the computing architecture to improve performance. Besides performance, reconfigurable hardware devices also achieve lower power consumption compared to General-Purpose Processors. Better performance and lower power consumption could be achieved using Application Specific Integrated Circuit (ASIC) technology. However, ASICs are not reconfigurable, turning them application specific. Reconfigurable logic becomes a major advantage when hardware flexibility permits to speed up whatever the application with the same hardware module. The first and most common devices utilized for reconfigurable computing are fine-grained FPGAs with a large hardware flexibility. To reduce the performance and area overhead associated with the reconfigurability, coarse-grained reconfigurable solutions has been proposed as a way to achieve better performance and lower power consumption. In this chapter we will provide a description of reconfigurable hardware for high performance computing.

A High-Performance and Low-Power On-Chip Network with Reconfigurable Topology

2010 ◽  
pp. 309-329
Author(s):  
Mehdi Modarressi ◽  
Hamid Sarbazi-Azad
Keyword(s):  
High Performance ◽  
Chip Multiprocessors ◽  
Optimization Methods ◽  
Critical Feature ◽  
Design Constraints ◽  
Core System ◽  
Traffic Pattern ◽  
On Chip ◽  
Network Mapping ◽  
Application Specific

In this chapter, we present a reconfigurable architecture for network-on-chips (NoC) on which arbitrary application-specific topologies can be implemented. The proposed NoC can dynamically tailor its topology to the traffic pattern of different applications, aiming to address one of the main drawbacks of existing application-specific NoC optimization methods, i.e. optimizing NoCs based on the traffic pattern of a single application. Supporting multiple applications is a critical feature of an NoC as several different applications are integrated into the modern and complex multi-core system-on-chips and chip multiprocessors and an NoC that is designed to run exactly one application does not necessarily meet the design constraints of other applications. The proposed NoC supports multiple applications by configuring as a topology which matches the traffic pattern of the currently running application in the best way. In this chapter, we first introduce the proposed reconfigurable topology and then address the two problems of core to network mapping and topology exploration. Experimental results show that this architecture effectively improves the performance of NoCs and reduces power consumption.

scholarly journals Automatic Segmentation and Enhancement of Pavement Cracks Based on 3D Pavement Images

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Baoxian Li ◽  
Kelvin C. P. Wang ◽  
Allen Zhang ◽  
Yue Fei ◽  
Giuseppe Sollazzo
Keyword(s):  
High Performance ◽  
Crack Detection ◽  
Matched Filter ◽  
Automatic Segmentation ◽  
Tensor Voting ◽  
Pavement Condition ◽  
Detection Algorithms ◽  
Pavement Surface ◽  
Saliency Maps ◽  
Inspection Systems

Pavement cracking is a significant symptom of pavement deterioration and deficiency. Conventional manual inspections of road condition are gradually replaced by novel automated inspection systems. As a result, a great amount of pavement surface information is digitized by these systems with a high resolution. With pavement surface data, pavement cracks can be detected using crack detection algorithms. In this paper, a fully automated algorithm for segmenting and enhancing pavement crack is proposed, which consists of four major procedures. First, a preprocessing procedure is employed to remove spurious noise and rectify the original 3D pavement data. Second, crack saliency maps are segmented from 3D pavement data using steerable matched filter bank. Third, 2D tensor voting is applied to crack saliency maps to achieve better curve continuity of crack structure and higher accuracy. Finally, postprocessing procedures are used to remove redundant noises. The proposed procedures were evaluated over 200 asphalt pavement images with diverse cracks. The experimental results demonstrated that the proposed method showed a high performance and could achieve average precision of 88.38%, recall of 93.15%, and F-measure of 90.68%, respectively. Accordingly, the proposed approach can be helpful in automated pavement condition assessment.

scholarly journals A Shift-Invariant Latent Variable Model for Automatic Music Transcription

2012 ◽  
Vol 36 (4) ◽  
pp. 81-94 ◽  
Author(s):  
Emmanouil Benetos ◽  
Simon Dixon
Keyword(s):  
Latent Variable ◽  
Markov Models ◽  
Variable Model ◽  
Data Set ◽  
Music Transcription ◽  
Transcription System ◽  
Automatic Music Transcription ◽  
Error Metrics ◽  
Frequency Modulations ◽  
Time Varying Pitch

In this work, a probabilistic model for multiple-instrument automatic music transcription is proposed. The model extends the shift-invariant probabilistic latent component analysis method, which is used for spectrogram factorization. Proposed extensions support the use of multiple spectral templates per pitch and per instrument source, as well as a time-varying pitch contribution for each source. Thus, this method can effectively be used for multiple-instrument automatic transcription. In addition, the shift-invariant aspect of the method can be exploited for detecting tuning changes and frequency modulations, as well as for visualizing pitch content. For note tracking and smoothing, pitch-wise hidden Markov models are used. For training, pitch templates from eight orchestral instruments were extracted, covering their complete note range. The transcription system was tested on multiple-instrument polyphonic recordings from the RWC database, a Disklavier data set, and the MIREX 2007 multi-F0 data set. Results demonstrate that the proposed method outperforms leading approaches from the transcription literature, using several error metrics.

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