Rate/Pitch modification of speech using the constant-Q transform

Youngberg, J.

doi:10.1109/icassp.1979.1170724

Cited by 12 publications

(2 citation statements)

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“…The other approach tries to play back every video frame as fast as possible, while making the best effort in keeping the audio track synchronized with video. Typically, this requires intelligent algorithms to adjust the sampling rate of the audio signal without losing its pitch information [21].…”

Section: Clientmentioning

confidence: 99%

Development of Columbia's video on demand testbed

Chang

Eleftheriadis

Anastassiou

1996

Signal Processing: Image Communication

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This paper reports on our progress in developing an advanced video-on-demand (VOD) testbed, which will accommodate various multimedia research and applications such as Electronic News on Demand, Columbia's Video Course Network, and Digital Libraries. Two different prototypes have been completed. The first generation of the testbed was based on a constant-bit-rate (CBR) video server utilizing Ethernet delivery. Contents were encoded and stored as MPEG-2 audio/video elementary streams. Software encoders/ decoders were used in content generation and playback. The second generation of the testbed was enhanced with the capability of transmitting true MPEG-2 transport streams over the campus ATM network as well as the wide-area NYNET ATM network. A real-time video pump and a distributed application control protocol (MPEG-2's DSM-CC) have been incorporated. Hardware decoders and set-tops are being incorporated to test wide-area video interoperability. Our VOD testbed also provides an advanced platform for implementing proof-of-concept prototypes of related research. Our current research focus covers video transmission with heterogeneous quality-of-service (QoS) provision, video storage architecture design, content-based video indexing and browsing, multi-resolution (MR) video coding, efficient manipulation of compressed video, and advanced user interfaces. An important aim is to enhance interoperability. Accommodation of practical multimedia applications and interoperability testing with external VOD systems are currently being undertaken.

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Section: Clientmentioning

confidence: 99%

Development of Columbia's video on demand testbed

Chang

Eleftheriadis

Anastassiou

1996

Signal Processing: Image Communication

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“…Such transform was introduced in [7], [8] and is referred to as Constant Q Transform (CQT). Similarly as in the DFT, the frequency range is divided into frequency bins, each represented by a band pass filter with a center frequency fk and filter bandwidth fk.…”

Section: Time-frequency Representationmentioning

confidence: 99%

Template music transcription for different types of musical instruments

Faruqe

Ahmad

Hasan

et al. 2010

2010 the 2nd International Conference on Computer and Automation Engineering (ICCAE)

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Automatically recovering the musical notation for a given signal allows reproducing and modifying the original performance. Music transcription can be considered as extracting the sequence of the notes that best models our audio signal. So it is understood as the process of analyzing a music signal in order to write down the parameters of the sounds occurring in it. Another principal application is structured audio coding: a MIDI-like representation which is highly compact but holds on to the identification and characteristics of a musical note to a significant extent. The applied notation can be the traditional musical notation which gives sufficient information for performing the piece using the available musical instruments. Automatic Music Transcription (extracting musical notes from an audio stream) is a complicated task due to the complexity of musical sounds that requires an effective solution. The scope of this article is in the automatic transcription of the harmonic and melodic parts of the real-world music signals. Algorithms are proposed that address two distinct sub-problems of music transcription. The main part of the article emphasizes the estimation of the several concurrent musical notes. The other sub-problem discusses musical meter estimation. This has to do with rhythmic aspects of music and refers to the estimation of the regular pattern of strong and weak beats in a piece of music. Signal processing methods for the automatic transcription of music are developed in this paper.

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Frequency Scaling of Speech Signals by Transform Techniques

Malah

Flanagan²

1981

Bell System Technical Journal

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The general framework of short‐time Fourier analysis, modification, and synthesis is used to describe in a unified way several known techniques for frequency scaling of speech signals. Subsequently, a frequency domain harmonic scaling technique is studied in detail with emphasis on improving its performance and its implementation efficiency. This technique is particularly attractive for 2:1 scaling by use of a sign tracking algorithm which avoids the need for explicit phase computation and unwrapping. The implementation efficiency is achieved by using the fast Fourier transform algorithm, embedded decimation and interpolation, and an extended version of a recently developed weighted overlap‐add synthesis scheme. The improvement in quality is achieved by improved sign tracking and elaborate design and selection of the analysis and synthesis prototype filters (data windows). Results of computer simulations, for a variety of adverse acoustical environment conditions, indicate that the system is highly robust but its quality for clean speech is lower than with a time domain harmonic scaling technique which uses pitch information. In applications which do not permit pitch transmission, a hybrid scheme which combines the two techniques is found to yield a better quality than either system alone.

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Rate/Pitch modification of speech using the constant-Q transform

Cited by 12 publications

References 1 publication

Development of Columbia's video on demand testbed

Development of Columbia's video on demand testbed

Template music transcription for different types of musical instruments

Frequency Scaling of Speech Signals by Transform Techniques

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