Detection of speech signal in strong ship-radiated noise based on spectrum entropy

Comparing the frequency spectrum distributions calculated from several successive frames, the change of the frequency spectrum of speech frames between successive frames is larger than that of the ship-radiated noise. The aim of this work is to propose a novel speech detection algorithm in strong ship-radiated noise. As inaccurate sentence boundaries are a major cause in automatic speech recognition in strong noise background. Hence, based on that characteristic, a new feature repeating pattern of frequency spectrum trend (RPFST) was calculated based on spectrum entropy. Firstly, the speech is detected roughly with the precision of 1 s by calculating the feature RPFST. Then, the detection precision is up to 20 ms, the length of frames, by method of frame shifting. Finally, benchmarked on a large measured data set, the detection accuracy (92 %) is achieved. The experimental results show the feasibility of the algorithm to all kinds of speech and ship-radiated noise

Detection of speech signal in strong ship-radiated noise based on spectrum entropy

Comparing the frequency spectrum distributions calculated from several successive frames, the change of the frequency spectrum of speech frames between successive frames is larger than that of the ship-radiated noise. The aim of this work is to propose a novel speech detection algorithm in strong ship-radiated noise. As inaccurate sentence boundaries are a major cause in automatic speech recognition in strong noise background. Hence, based on that characteristic, a new feature repeating pattern of frequency spectrum trend (RPFST) was calculated based on spectrum entropy. Firstly, the speech is detected roughly with the precision of 1 s by calculating the feature RPFST. Then, the detection precision is up to 20 ms, the length of frames, by method of frame shifting. Finally, benchmarked on a large measured data set, the detection accuracy (92 %) is achieved. The experimental results show the feasibility of the algorithm to all kinds of speech and ship-radiated noise

Music in the Real World: Live Music Retrieval and the Limitations Thereof

Music is everywhere around us. Cliché, yes, but music has found a way to infiltrate nearly every field of study known to man. Writers and authors include musical references to give context to novels and works of literature. Physicists study frequencies of music and sound to determine their effects on animals and humans. The field of Information Science is no different when it comes to its relationship with music. Music Information Retrieval (MIR), a subset of Information and Library Science, was founded in 2000 to gain a better understanding of the information music contains and how this can best be extracted for human use (Byrd, Fingerhunt, 2002). MIR deals with both the metadata of music (band, lyrics, album name, etc.) and actual content of music (chord structure, melodic design, and rhythmic analysis). As the title suggests, a large part of MIR deals with retrieval, which is done by both humans and machines. Examples of retrieval include the applications of SoundHound and Shazam, which make use of audio fingerprinting technology in order to query databases that contain songs in order for a user to identify a song quickly and efficiently. As stated above, the field of MIR is relatively new with respect to academia, with less than 15 years worth of research and information available. Although the term itself was originally coined in a lecture in the late 1960s, the field itself did not receive formal recognition until a much later time. Because of this, many areas of MIR have not been studied to their fullest extent. One of these areas, content-based music retrieval in a live environment, has seen painfully little research. It is this area I wish to study more in-depth by posing the following questions: How successful are current content-based music identification systems at identifying jazz songs performed live? In cases where they fail, what are the possible causes of those failures? What can be done to improve the effectiveness of these applications and methods and what does this imply for future research? As I stated before, the lack of research done for live music applications leads me to believe that more should be done. The need for identification of tracks occurs more often in live venues, like concerts, more so than studio recordings How many times have you heard from friends or neighbors, “What song is this? Do you know this one?” By looking at identification methods and their limitations, research can address these issues and hopefully design more efficient applications.Bachelor of Scienc

Recognition of activities of daily living

Activities of daily living (ADL) are things we normally do in daily living, including any daily activity such as feeding ourselves, bathing, dressing, grooming, work, homemaking, and leisure. The ability or inability to perform ADLs can be used as a very practical measure of human capability in many types of disorder and disability. Oftentimes in a health care facility, with the help of observations by nurses and self-reporting by residents, professional staff manually collect ADL data and enter data into the system. Technologies in smart homes can provide some solutions to detecting and monitoring a resident’s ADL. Typically multiple sensors can be deployed, such as surveillance cameras in the smart home environment, and contacted sensors affixed to the resident’s body. Note that the traditional technologies incur costly and laborious sensor deployment, and cause uncomfortable feeling of contacted sensors with increased inconvenience. This work presents a novel system facilitated via mobile devices to collect and analyze mobile data pertaining to the human users’ ADL. By employing only one smart phone, this system, named ADL recognition system, significantly reduces set-up costs and saves manpower. It encapsulates rather sophisticated technologies under the hood, such as an agent-based information management platform integrating both the mobile end and the cloud, observer patterns and a time-series based motion analysis mechanism over sensory data. As a single-point deployment system, ADL recognition system provides further benefits that enable the replay of users’ daily ADL routines, in addition to the timely assessment of their life habits