The digitalization of audio is commonly implemented for the purpose of convenient storage and transmission of music and songs in today's digital age. Analyzing digital audio for an insightful look at a specific musical characteristic, however, can be quite challenging for various types of applications. Many existing musical analysis techniques can examine a particular piece of audio data. For example, the frequency of digital sound can be easily read and identified at a specific section in an audio file. Based on this information, we could determine the musical note being played at that instant, but what if you want to see a list of all the notes played in a song? While most existing methods help to provide information about a single piece of the audio data at a time, few of them can analyze the available audio file on a larger scale. The research conducted in this work considers how to further utilize the examination of audio data by storing more information from the original audio file. In practice, we develop a novel musical analysis system Musicians Aid to process musical representation and examination of audio data. Musicians Aid solves the previous problem by storing and analyzing the audio information as it reads it rather than tossing it aside. The system can provide professional musicians with an insightful look at the music they created and advance their understanding of their work. Amateur musicians could also benefit from using it solely for the purpose of obtaining feedback about a song they were attempting to play. By comparing our system's interpretation of traditional sheet music with their own playing, a musician could ensure what they played was correct. More specifically, the system could show them exactly where they went wrong and how to adjust their mistakes. In addition, the application could be extended over the Internet to allow users to play music with one another and then review the audio data they produced. This would be particularly useful for teaching music lessons on the web. The developed system is evaluated with songs played with guitar, keyboard, violin, and other popular musical instruments (primarily electronic or stringed instruments). The Musicians Aid system is successful at both representing and analyzing audio data and it is also powerful in assisting individuals interested in learning and understanding music.
Streaming media is a recent technique for delivering multimedia information from a source provider to an end- user over the Internet. The major advantage of this technique is that the media player can start playing a multimedia file even before the entire file is transmitted. Most streaming media applications are currently implemented based on the client-server architecture, where a server system hosts the media file and a client system connects to this server system to download the file. Although the client-server architecture is successful in many situations, it may not be ideal to rely on such a system to provide the streaming service as users may be required to register an account using personal information in order to use the service. This is troublesome if a user wishes to watch a movie simultaneously while interacting with a friend in another part of the world over the Internet. In this paper, we describe a new real-time media streaming application implemented on a peer-to-peer (P2P) architecture in order to overcome these challenges within a mobile environment. When using the peer-to-peer architecture, streaming media is shared directly between end-users, called peers, with minimal or no reliance on a dedicated server. Based on the proposed software pεvμa (pronounced [revma]), named for the Greek word meaning stream, we can host a media file on any computer and directly stream it to a connected partner. To accomplish this, pεvμa utilizes the Microsoft .NET Framework and Windows Presentation Framework, which are widely available on various types of windows-compatible personal computers and mobile devices. With specially designed multi-threaded algorithms, the application can stream HD video at speeds upwards of 20 Mbps using the User Datagram Protocol (UDP). Streaming and playback are handled using synchronized threads that communicate with one another once a connection is established. Alteration of playback, such as pausing playback or tracking to a different spot in the media file, will be reflected in all media streams. These techniques are designed to allow users at different locations to simultaneously view a full length HD video and interactively control the media streaming session. To create a sustainable media stream with high quality, our system supports UDP packet loss recovery at high transmission speed using custom File- Buffers. Traditional real-time streaming protocols such as Real-time Transport Protocol/RTP Control Protocol (RTP/RTCP) provide no such error recovery mechanism. Finally, the system also features an Instant Messenger that allows users to perform social interactions with one another while they enjoy a media file. The ultimate goal of the application is to offer users a hassle free way to watch a media file over long distances without having to upload any personal information into a third party database. Moreover, the users can communicate with each other and stream media directly from one mobile device to another while maintaining an independence from traditional sign up required by most streaming services.
Developments in rapid acquisition techniques and reconstruction algorithms, such as sensitivity encoding (SENSE)
for MR images and fan-beam filtered backprojection (fFBP) for CT images, have seen widely applications in
medical imaging in recent years. Nevertheless, such techniques introduce spatially varying noise levels in the
reconstructed medical images that may degrade the image quality and hinder subsequent diagnostic inspection.
Though this may be alleviated with multiple scanning images or the sensitivity profiles of imaging device, these
pieces of information are typically unavailable in clinical practice. In this work, we describe a novel local noise
level estimation technique based on the near constancy of kurtosis of medical image in band-pass filtered domain.
This technique can effectively estimate noise levels in the pixel domain and recover the noise map for
reconstructed medical images with nonuniform noise distribution. The advantage of this method is that it requires
no prior knowledge of the imaging devices and can be implemented when only one single medical image is
available. We report experiments that demonstrate the effectiveness of the proposed method in estimating the
local noise levels for medical images quantitatively and qualitatively, and compare its estimation performance
to another recent developed blind noise estimation approach. Finally, we also evaluate the practical denoising
performance of our noise estimation algorithm on medical images when it is used as a front-end to a denoiser
that uses principal component analysis with local pixel grouping (LPG-PCA) technique.