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Recent neuroscience research has shown increasing use of multivariate decoding methods and machine learning. These methods, by uncovering the source and nature of informative variance in large data sets, invert the classical direction of inference that attempts to explain brain activity from mental state variables or stimulus features. However, these techniques are not yet commonly used among music researchers. In this position article, we introduce some key features of machine learning methods and review their use in the field of cognitive and behavioral neuroscience of music. We argue for the great potential of these methods in decoding multiple data types, specifically audio waveforms, electroen- cephalography, functional MRI, and motion capture data. By finding the most informative aspects of stimulus and performance data, hypotheses can be generated pertaining to how the brain processes incoming musical information and generates behavioral output, respectively. Importantly, these methods are also applicable to different neural and physiological data types such as magnetoencephalography, near-infrared spectroscopy, positron emission tomography, and electromyography.Keywords: machine learning, classification, music neuroscienceSupplemental materials: http://dx.doi.org/10.1037/a0031014.suppMusic is acoustic information with complex temporal and spa- tial features. Research into perception and cognition of multifac- eted aspects of music aims to decode the information from neural signals elicited by listening to music. Music performance, on the other hand, entails the encoding of musical information to neural commands issued to the muscles. To understand the neural pro- cesses underlying music perception, cognition, and performance, therefore, researchers face issues of extracting meaningful infor- mation from extremely large data sets with regard to neural, physiological, and biomechanical signals. This is nontrivial in light of recent technological advances in data collection, which can lead to a potentially overwhelming amount of data. The supervised and unsupervised methods of machine learning are powerful tools for uncovering unseen patterns in these large data sets. In this way, not only can the means of specified conditions be compared, but data-driven methods are used to uncover sources of informative variance in the signals. Moreover, machine learning allows for quantitative evaluation of individual differences in music percep- tion and performance.In this article, we introduce key features of machine learning and highlight some examples of their use on a range of data types. After reviewing basic concepts and terminology, we discuss di- mensionality reduction and the impact of the choice of algorithm. We then turn to data types we judge to be most relevant to the neural processing of music. For an audio waveform, it is possible to elucidate the most perceptually informative part of the signal, by determining which aspects of the signal are most salient or useful to the brain in determining specific characteristics of the sound. In the same way, it is possible to uncover neural representations of musical attributes such as rhythm and harmony in a data-driven way by applying supervised and unsupervised learning to single- trial electroencephalography (EEG) or functional MRI (fMRI) data. Finally, machine learning methods are also useful in behav- ioral research, allowing characterization of fundamental patterns of movements that use a large number of joints and muscles during musical performance.MethodsAlthough we do not aim to give a complete overview of these methods here (for a detailed review of machine learning methods for brain imaging, see Lemm, Blankertz, Dickhaus, & Muller, 2011), we introduce a number of key aspects of machine learning as they pertain to music cognition research.Basic TerminologyMachine learning (or statistical learning) involves uncovering meaningful patterns in collections of observations, often with the goal of classifying, or categorizing, the observations in some way. …

This chapter explains the basic concepts in computational methods used for analysis of sound scenes and events. Even though the analysis tasks in many applications seem different, the underlying computational methods are typically based on the same principles. We explain the commonalities between analysis tasks such as sound event detection, sound scene classification, or audio tagging. We focus on the machine learning approach, where the sound categories (i.e., classes) to be analyzed are defined in advance. We explain the typical components of an analysis system, including signal pre-processing, feature extraction, and pattern classification. We also preset an example system based on multi-label deep neural networks, which has been found to be applicable in many analysis tasks discussed in this book. Finally, we explain the whole processing chain that involves developing computational audio analysis systems.

Around the year 2015, new tidings began to be heard across the Lebanese coastline as local marine biologists—receiving reports from divers and fishermen—became aware of a new phenomenon in the coastal waters. Pterois miles, the lionfish, samakat al-asad, a species not usually found in the waters, were being encountered in rapidly increasing numbers. Over the following years, as other Mediterranean countries started reporting similar occurrences, a sense of alert slowly built. Dina, one of my interlocutors and a shining young marine ecologist, had been at work setting up a new NGO to conduct marine pedagogy and environmental awareness campaigns. As the invasion proceeded apace, she and her group reacted to the invading fish swarming the Lebanese coastal waters, putting together a counter-campaign. Meetings were called, ideas pitched, and plans convened. The invasion had to be confronted, just as Elias, another interlocutor from Dina's group, a hobbyist diver in his late 20s and active participant in civil society campaigns, told me during an interview in late 2018: Now the hot topic is invasive species, like the lionfish, and the pufferfish. We have seen lionfish for maybe three years, but now people are starting to talk about it. They are in huge numbers, huge numbers, right. I remember the first couple of dives I did, used to dive with a friend of mine. She had a different diving certificate; I had an 18-meter-depth limit while she had 40-meter limit. She used to tell me how beautiful the lionfish she encountered below was. They spent minutes watching how beautiful the creature is. Now, we see them every dive. If there is no lionfish around, there is like something wrong. Yesterday, during the weekend, we dived next to one spot, next to the Casino du Liban. It was the first time I saw a lionfish and it was a huge fish. They reproduce in huge amounts, huge amounts.

Unna Saiva is a Sami offering site situated in Gallivare in Northern Sweden. The site was excavated in the early 20th century. It yielded a large number of finds, including objects of silver, pewter and other metals, coins, and animal bones. The metal objects and coins date mainly to the late 10th century and 11th century AD, whereas the animal bone finds date from the 6th to the 17th centuries AD. Zooarchaeological analysis, radiocarbon datings of animal bones and stable isotope analyses conducted in this study reveal new information about religious ritual, religious change, and human–animal relationships among the Sami. We argue that there was a change in the offering tradition, intertwining with changes in the subsistence economy and especially reindeer domestication. Our results indicate that reindeer domestication, acknowledged to have had a major impact on social organization and economy, was also a major factor in the transformation of Sami indigenous religion. However, the underlying nature of the offering tradition remained consistent although the focal species of economic and religious interest changed.

The idea of the article is to integrate knowledge management thinking to triangulation logic. Triangulation logic can be seen as a quality control mechanism in the field of knowledge management. Triangulation logic can also see as a critical knowledge management mechanism, which helps integrate inductive and deductive thinking in scientific projects. Triangulation is a vehicle for cross validation when two or more distinct methods are found to be congruent and yield comparable data. Today various scholars have noted that qualitative and quantitative methods should be viewed as complementary rather than as rival approaches. In fact, most textbooks underscore the desirability of mixing methods given the strengths and weaknesses found in single mono-method designs. In this kind of scientific discussion, the triangulation logic, which integrates quantitative and qualitative methods, is a highly relevant knowledge management issue. Today future-oriented foresight research is mostly based on quantitative and qualitative analyses. The three key elements of foresight activity are diagnosis (hindsight), prognosis (foresight) and description (decision support). In order to integrate triangulation logic to these foresight activities, experts and knowledge management scientists must think carefully alternative strategies, how to build up logical link between triangulation logic and foresight activities. This article provides some new ideas, how these novel knowledge management strategies could be constructed, when we look KM issues from broader triangulation perspective.

article i nfo Late Holocene sediments in the Baltic Sea provide an opportunity to study lateral changes in the assemblages of identifiable biogenic sedimentary structures (ichnofossils) in a large, high-latitude semi-enclosed sea with instrumentally determined gradients in biodiversity and environmental factors such as salinity and ox- ygen availability. Integrated sedimentological and ichnological analysis is carried out on 6 long cores collect- ed along an open-sea, declining salinity transect across the basin. Muddy sediments in euhaline (Kattegat) and polyhaline (Mecklenburg Bight) sites are characterized by the archetypal Cruziana Ichnofacies, portrayed by subsurface deposit-feeding structures (Scolicia and Planolites), surface deposit-feeding structures (Sko- lithos), and structures that reflect both these feeding strategies (Palaeophycus, Arenicolites/Polykladichnus and unnamed biodeformational structures produced by bivalves). The ichnofossils are tiered to 3 levels. The Cruziana Ichnofacies is impoverished in the higher mesohaline Arkona Basin and even more so with de- clining salinity farther inland. The deepest, oxygen-restricted study sites (Gotland Deep and the western Gulf of Finland) below a permanent halocline are characterized by very small and shallow deposit-feeding struc- tures (Planolites and rare flat Arenicolites/Polykladichnus), and poorly developed tiering. The nearly freshwa- ter eastern Gulf of Finland is characterized by the Cenozoic archetypal Mermia Ichnofacies, dominated by narrow and shallow subsurface and surface deposit-feeding structures (Planolites and flat Arenicolites/ Polykladichnus). Large Planolites (3-7 mm in diameter) at this site are untypical of Mermia Ichnofacies assem- blages. These results confirm the earlier observations that marine forms dominate brackish-water ichnoas- semblages, with the ichnofossil size and diversity decreasing with declining salinity. The results also confirm the predicted decreases in the ichnofossil size and vertical extent at low-oxygen levels. Poorly devel- oped tiering is a particularly useful indicator of oxygen stress in the salinity-restricted system.

Creaky voice, also referred to as vocal fry, is a voice quality frequently produced in many languages, in both read and conversational speech. In order to enhance the naturalness of speech synthesisers, these latter should be able to generate speech in all its expressive diversity. This includes a proper use of creaky voice. The goal of this paper is two-fold. Firstly we analyse how contextual factors can be informative for the prediction of creaky use. It is observed that a few contextual factors related to speech production preceding a silence or a pause are of particular interest. This study validates that creaky voice plays a crucial syntactic role, allowing for a better structuring of phrases. In a second experiment, we investigate the prediction of creakiness from contextual factors based on HMMs. Four methods are compared on a US English and a Finnish speaker. It is shown that the best prediction technique achieves a promising performance comparable to what is carried out with the creaky detection algorithm on which HMMs were trained.