There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Neurons in the brain communicate by short electrical pulses, the so-called action potentials or spikes. How can we understand the process of spike generation? How can we understand information transmission by neurons? What happens if thousands of neurons are coupled together in a seemingly random network? How does the network connectivity determine the activity patterns? And, vice versa, how does the spike activity influence the connectivity pattern? These questions are addressed in this 2002 introduction to spiking neurons aimed at those taking courses in computational neuroscience, theoretical biology, biophysics, or neural networks. The approach will suit students of physics, mathematics, or computer science; it will also be useful for biologists who are interested in mathematical modelling. The text is enhanced by many worked examples and illustrations. There are no mathematical prerequisites beyond what the audience would meet as undergraduates: more advanced techniques are introduced in an elementary, concrete fashion when needed.

/pdf/spiking-neuron-models-single-neurons-populations-plasticity-58gzrd6ih6.pdf

Spiking Neuron Models: Single Neurons, Populations, Plasticity

Musical genres are categorical labels created by humans to characterize pieces of music. A musical genre is characterized by the common characteristics shared by its members. These characteristics typically are related to the instrumentation, rhythmic structure, and harmonic content of the music. Genre hierarchies are commonly used to structure the large collections of music available on the Web. Currently musical genre annotation is performed manually. Automatic musical genre classification can assist or replace the human user in this process and would be a valuable addition to music information retrieval systems. In addition, automatic musical genre classification provides a framework for developing and evaluating features for any type of content-based analysis of musical signals. In this paper, the automatic classification of audio signals into an hierarchy of musical genres is explored. More specifically, three feature sets for representing timbral texture, rhythmic content and pitch content are proposed. The performance and relative importance of the proposed features is investigated by training statistical pattern recognition classifiers using real-world audio collections. Both whole file and real-time frame-based classification schemes are described. Using the proposed feature sets, classification of 61% for ten musical genres is achieved. This result is comparable to results reported for human musical genre classification.

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Musical genre classification of audio signals

The analysis of speech in different temporal integration windows: cerebral lateralization as 'asymmetric sampling in time'

Most time series data mining algorithms use similarity search as a core subroutine, and thus the time taken for similarity search is the bottleneck for virtually all time series data mining algorithms. The difficulty of scaling search to large datasets largely explains why most academic work on time series data mining has plateaued at considering a few millions of time series objects, while much of industry and science sits on billions of time series objects waiting to be explored. In this work we show that by using a combination of four novel ideas we can search and mine truly massive time series for the first time. We demonstrate the following extremely unintuitive fact; in large datasets we can exactly search under DTW much more quickly than the current state-of-the-art Euclidean distance search algorithms. We demonstrate our work on the largest set of time series experiments ever attempted. In particular, the largest dataset we consider is larger than the combined size of all of the time series datasets considered in all data mining papers ever published. We show that our ideas allow us to solve higher-level time series data mining problem such as motif discovery and clustering at scales that would otherwise be untenable. In addition to mining massive datasets, we will show that our ideas also have implications for real-time monitoring of data streams, allowing us to handle much faster arrival rates and/or use cheaper and lower powered devices than are currently possible.

/pdf/searching-and-mining-trillions-of-time-series-subsequences-2kv2u7mhup.pdf

Searching and mining trillions of time series subsequences under dynamic time warping

The decrease in detection and discrimination thresholds with increases in signal duration has often been taken to indicate that a process of relatively long‐term temporal integration occurs in hearing. Two experiments are reported that suggest that no such process occurs. The first experiment is similar to the two‐pulse experiment reported by Zwislocki [J. Zwislocki, J. Acoust. Soc. Am. 32, 1046–1059 (1960)] in which the threshold in quiet for a pair of brief pulses is measured as a function of the temporal separation between them. Our data indicate that power integration occurs only for separations less than approximately 5 ms. For separations larger than 5–10 ms, thresholds do not change with separation and the pulses appear to be processed independently. In the second experiment, brief 1‐kHz tone pulses separated by 100 ms are presented during gaps in a wideband noise. The threshold for a pair of pulses is lower than that for either pulse presented alone, indicating that some type of ‘‘integration’’ oc...

Temporal integration and multiple looks.

An important application for use with multimedia databases is a browsing aid, which allows a user to quickly and efficiently preview selections from either a database or from the results of a database query. Methods for facilitating browsing, though, are necessarily media dependent. We present one such method that produces short, representative samples (or "audio thumbnails") of selections of popular music. This method attempts to identify the chorus or refrain of a song by identifying repeated sections of the audio waveform. A reduced spectral representation of the selection based on a chroma transformation of the spectrum is used to find repeating patterns. This representation encodes harmonic relationships in a signal and thus is ideal for popular music, which is often characterized by prominent harmonic progressions. The method is evaluated over a sizable database of popular music and found to perform well, with most of the errors resulting from songs that do not meet our structural assumptions.

/pdf/to-catch-a-chorus-using-chroma-based-representations-for-9p2xvo0k12.pdf

To catch a chorus: using chroma-based representations for audio thumbnailing

With the growing prevalence of large databases of multimedia content, methods for facilitating rapid browsing of such databases or the results of a database search are becoming increasingly important. However, these methods are necessarily media dependent. We present a system for producing short, representative samples (or "audio thumbnails") of selections of popular music. The system searches for structural redundancy within a given song with the aim of identifying something like a chorus or refrain. To isolate a useful class of features for performing such structure-based pattern recognition, we present a development of the chromagram, a variation on traditional time-frequency distributions that seeks to represent the cyclic attribute of pitch perception, known as chroma. The pattern recognition system itself employs a quantized chromagram that represents the spectral energy at each of the 12 pitch classes. We evaluate the system on a database of popular music and score its performance against a set of "ideal" thumbnail locations. Overall performance is found to be quite good, with the majority of errors resulting from songs that do not meet our structural assumptions.

/pdf/audio-thumbnailing-of-popular-music-using-chroma-based-3fn5tnephg.pdf

Audio thumbnailing of popular music using chroma-based representations

In this tutorial, head-related transfer functions (HRTFs) are introduced and treated with respect to their role in the synthesis of spatial sound over headphones. HRTFs are formally defined, and are shown to be important in reducing the ambiguity with which the classical duplex theory decodes a free-field sound's spatial location. Typical HRTF measurement strategies are described, and simple applications of HRTFs to headphone-based spatialized sound synthesis are given. By comparing and contrasting representations of HRTFs in the time, frequency, and spatial domains, different analytic and signal processing techniques used to investigate the structure of HRTFs are highlighted.

Introduction to Head-Related Transfer Functions (HRTFs): Representations of HRTFs in Time, Frequency, and Space

This study investigated the relationship between electrode discrimination and speech recognition in 11 postlingually deafened adult cochlear implant subjects who were implanted with the Nucleus/Cochlear Corporation multichannel device. The discriminability of each electrode included in a subject’s clinical map was measured using adaptive and fixed-level discrimination tasks. Considerable variability in electrode discriminability was observed across subjects. Two subjects could discriminate all electrodes, and discrimination performance by the remaining nine subjects varied from near perfect to very poor. In these nine subjects, the results obtained from the discrimination tasks were used to create a map that contained only discriminable electrodes, and subjects’ performance on speech recognition tasks using this experimental map was measured. Four different speech recognition tests were administered: a nine-choice closed-set medial vowel recognition task, a 14-choice closed-set medial consonant recognitio...

Gregory H. Wakefield

Papers

Temporal integration and multiple looks.

To catch a chorus: using chroma-based representations for audio thumbnailing

Audio thumbnailing of popular music using chroma-based representations

Introduction to Head-Related Transfer Functions (HRTFs): Representations of HRTFs in Time, Frequency, and Space

Electrode discrimination and speech recognition in postlingually deafened adult cochlear implant subjects.