Athletic performance sensing and/or tracking systems include components for measuring or sensing athletic performance data and/or for storing and/or displaying desired information associated with the athletic performance to the user (or others). Such systems can allow users a wide variety of options in creating workouts, selecting and presenting media content during the athletic performance, etc., e.g., to help keep users entertained and motivated. In some instances, user feedback may be used, optionally in combination with objective data relating to a workout, to control features of the workout routine, to control the music or other media content selected and/or presented, and/or to control features of future workout routines and/or the presented media content.

Athletic performance sensing and/or tracking systems and methods

Athletic performance monitoring systems and methods, many of which utilize, in some manner, global positioning satellite (“GPS”) data, provide data and information to athletes and/or to equipment used by athletes during an athletic event. Such systems and methods may provide route information to athletes and/or their trainers, e.g., for pre-event planning, goal setting, and calibration purposes. Such systems and methods optionally may provide real time information to the athlete while the event takes place, e.g., to assist in reaching the pre-set goals. Additionally, data and information collected by such systems and methods may assist in post-event analysis for athletes and their trainers, e.g., to evaluate past performances and to assist in improving future performances.

Multi-sensor monitoring of athletic performance

We present a computer audition system that can both annotate novel audio tracks with semantically meaningful words and retrieve relevant tracks from a database of unlabeled audio content given a text-based query. We consider the related tasks of content-based audio annotation and retrieval as one supervised multiclass, multilabel problem in which we model the joint probability of acoustic features and words. We collect a data set of 1700 human-generated annotations that describe 500 Western popular music tracks. For each word in a vocabulary, we use this data to train a Gaussian mixture model (GMM) over an audio feature space. We estimate the parameters of the model using the weighted mixture hierarchies expectation maximization algorithm. This algorithm is more scalable to large data sets and produces better density estimates than standard parameter estimation techniques. The quality of the music annotations produced by our system is comparable with the performance of humans on the same task. Our ldquoquery-by-textrdquo system can retrieve appropriate songs for a large number of musically relevant words. We also show that our audition system is general by learning a model that can annotate and retrieve sound effects.

/pdf/semantic-annotation-and-retrieval-of-music-and-sound-effects-14hge5xy7b.pdf

Semantic Annotation and Retrieval of Music and Sound Effects

A method is provided for modifying an image. The method comprises displaying an image, the image comprising a portion of an object; and determining if an edge of the object is in a location within the portion. The method further comprises detecting movement, in a member direction, of an operating member with respect to the edge. The method still further comprises moving, if the edge is not in the location, the object in an object direction corresponding to the detected movement; and modifying, if the edge is in the location, the image in response to the detected movement, the modified image comprising the edge in the location.

Information processing apparatus, information processing method and program

This paper surveys the state of the art in automatic emotion recognition in music. Music is oftentimes referred to as a “language of emotion” [1], and it is natural for us to categorize music in terms of its emotional associations. Myriad features, such as harmony, timbre, interpretation, and lyrics affect emotion, and the mood of a piece may also change over its duration. But in developing automated systems to organize music in terms of emotional content, we are faced with a problem that oftentimes lacks a welldefined answer; there may be considerable disagreement regarding the perception and interpretation of the emotions of a song or ambiguity within the piece itself. When compared to other music information retrieval tasks (e.g., genre identification), the identification of musical mood is still in its early stages, though it has received increasing attention in recent years. In this paper we explore a wide range of research in music emotion recognition, particularly focusing on methods that use contextual text information (e.g., websites, tags, and lyrics) and content-based approaches, as well as systems combining multiple feature domains.

https://ismir2010.ismir.net/proceedings/ismir2010-45.pdf

Music emotion recognition: A state of the art review

Parametric-stereo coding is a technique to efficiently code a stereo audio signal as a monaural signal plus a small amount of parametric overhead to describe the stereo image. The stereo properties are analyzed, encoded, and reinstated in a decoder according to spatial psychoacoustical principles. The monaural signal can be encoded using any (conventional) audio coder. Experiments show that the parameterized description of spatial properties enables a highly efficient, high-quality stereo audio representation.

/pdf/parametric-coding-of-stereo-audio-4fznz7nwhj.pdf

Parametric coding of stereo audio

In summary, this application describes a psycho-acoustically motivated, parametric description of the spatial attributes of multichannel audio signals. This parametric description allows strong bitrate reductions in audio coders, since only one monaural signal has to be transmitted, combined with (quantized) parameters which describe the spatial properties of the signal. The decoder can form the original amount of audio channels by applying the spatial parameters. For near-CD-quality stereo audio, a bitrate associated with these spatial parameters of 10 kbit/s or less seems sufficient to reproduce the correct spatial impression at the receiving end.

pARAMETRIC REPRESENTATION OF SPATIAL AUDIO

Multi-channel audio signals are coded into a monaural audio signal and information allowing to recover the multi-channel audio signal from the monaural audio signal and the information. The information is generated by determining a first portion of the information for a first frequency region of the multi-channel audio signal, and by determining a second portion of the information for a second frequency region of the multi-channel audio signal. The second frequency region is a portion of the first frequency region and thus is a sub-range of the first frequency region. The information is multi-layered enabling a scaling of the decoding quality versus bit rate.

Parametric multi-channel audio representation

The audio reproduction apparatus (100) for sports training purposes comprises a tempo derivation unit (103) for deriving a selected tempo (T) on the basis of a data signal (dl, d2, d3) e.g. from a sports measurement device such as a heart rate meter; and an audio conditioning unit (104) arranged to deliver based on the input audio signal the output audio signal, with a tempo within a predefined accepted deviation from the selected tempo (T), whereby the audio conditioning unit (104) comprises a tempo calculation unit (106) arranged to calculate an input tempo (TI) of the input audio signal, and the audio conditioning unit (104) is arranged to deliver the output signal in dependence of the input tempo (TI).

Audio reproduction apparatus, method, computer program

The use of psychoacoustical masking models for audio coding applications has been wide spread over the past decades. In such applications, it is typically assumed that the original input signal serves as a masker for the distortions that are introduced by the lossy coding method that is used. Such masking models are based on the peripheral bandpass filtering properties of the auditory system and basically evaluate the distortion-to-masker ratio within each auditory filter. Up to now these models have been based on the assumption that the masking of distortions is governed by the auditory filter for which the ratio between distortion and masker is largest. This assumption, however, is not in line with some new findings within the field of psychoacoustics. A more accurate assumption would be that the human auditory system is able to integrate distortions that are present within a range of auditory filters. In this contribution a new model is presented which is in line with new psychoacoustical studies and which is suitable for application within an audio codec. Although this model can be used to derive a masking curve, the model also gives a measure for the detectability of distortions provided that distortions are not too large.

Steven van de Par

Papers

Parametric coding of stereo audio

pARAMETRIC REPRESENTATION OF SPATIAL AUDIO

Parametric multi-channel audio representation

Audio reproduction apparatus, method, computer program

A new psychoacoustical masking model for audio coding applications