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The mission of Montessori Western Teacher Training Program is to facilitate the development of the “whole” adult. Our approach is to present the Montessori Method as an open-ended system designed to foster autonomy. We support this model by providing a related group of varied experiences that are congruent, on an adult level, with the child’s experience and growth in a Montessori setting.

Various Timbri Musicali N4

  • This article walks through some Frequently Asked Questions about the Mixtrack 3. The symphony is divided into four movements with the following tempo markings :. From a more general stance, the presented evaluation, using five normalization conditions Various Timbri Musicali N4 three acoustic feature sets, indicates that one should not overestimate the universality Various Timbri Musicali N4 distinct acoustic features. It is the first truly pro-grade DJ controller and standalone 4-channel mixer with effects, allowing you to mix external sources with tracks on your computer. Instantly plug in your preferred device iPhone, iPad, iPod touch, or laptop and install the included software to start the party! Acoustic and categorical dissimilarity of musical timbre: evidence from asymmetries between acoustic and chimeric sounds.

    In this guide we will cover the setup of both versions in Virtual DJ. This article walks you through how to setup your headphone cue with multiple OS platforms.

    This is not a hardware-caused symptom and is due to some adjustments that need to be made in the app on iOS 8.

    If you experience these symptoms, follow the steps suggested in this guide and you will be back up and running in no time! We've put together answers to some of the most common questions about the Numark iDJ Pro controller.

    With the tips in this FAQ under your belt, you'll have even better control over your new hardware and software. One of the coolest features of the Numark N4 is timecode streaming.

    You can use your choice of timecode CD's or vinyl to control another two decks. To help ensure that you are up and running, this guide walks through the steps to make sure that your N4 hardware and software are configured correctly and ready to go. The original NS6 was conceived and built at a time when the 4-channel standalone DJ controller did not yet exist.

    We knew you wanted it, and Numark was the first to bring it to you. The Numark NS6 offers pro-level touch-sensitive platter control over four software playback decks.

    This guide walks through how to setup your brand new NS6 with your computer. The Numark NS6 is a 4-channel Digital DJ controller with a complete built-in mixer and four decks of software control. It is the first truly pro-grade DJ controller and standalone 4-channel mixer with effects, allowing you to mix external sources with tracks on your computer. This article walks through the Numark NS6 driver installation process.

    The Numark NS6 allows for a huge variety of creativity as well as easy workflow. This guide walks through the setup of the Numark NS6 with Traktor 2. The Numark NS6 offers pro level touch-sensitive platter control over four software playback decks.

    This article covers Frequently Asked Questions, setup difficulty, firmware updates, and basic troubleshooting. It is a premier four-deck controller which features three high-resolution color screens with a stack-able waveform display and an interactive control surface.

    This guide explains the update procedure for the firmware that can be found on the Numark NS7II product page. Sometimes, other programs or audio configurations may prevent this from happening. We walk through a number of them in this article. It is a premier four-deck controller which features three high-resolution color screens with a stackable waveform display and an interactive control surface. In this article we answer the frequently asked questions that many users want to know.

    NS7 combines an all-metal chassis with adjustable torque, motorized, aluminum-turntable platters, vinyl, and a professional audio interface to deliver a complete performance solution that will satisfy even the most hardcore turntablist. We also answer the frequently asked questions that many users want to know. The FX control on this unit is truly one of a kind. Some parts of this controller have been designed to be user-replaceable. This guide is a step-by-step walk through on how to replace the NS7II crossfader.

    Serato's Flip expansion pack is a great tool for those using Serato DJ 1. However, the importance of timbre in listening tasks that involve short excerpts has not yet been demonstrated empirically. Hence, the goal of this study was to develop a method that allows to explore to what degree similarity judgments of short music clips can be modeled with low-level acoustic features related to timbre.

    We utilized the similarity data from two large samples of participants: Sample I was obtained via an online survey, used 16 clips of ms length, and contained responses of , participants. Sample II was collected in a lab environment, used 16 clips of ms length, and contained responses from participants.

    Our model used two sets of audio features which included commonly used timbre descriptors and the well-known Mel-frequency cepstral coefficients as well as their temporal derivates. In order to predict pairwise similarities, the resulting distances between clips in terms of their audio features were used as predictor variables with partial least-squares regression. We found that a sparse selection of three to seven features from both descriptor sets—mainly encoding the coarse shape of the spectrum as well as spectrotemporal variability—best predicted similarities across the two sets of sounds.

    Overall, the results of this study empirically demonstrate that both acoustic features related to timbre as well as higher level categorical features such as musical genre play a major role in the perception of short music clips. There is growing evidence that human listeners are able to instantly categorize short music clips containing complex mixtures of sounds, e. Even more, the information contained in clips lasting only a few hundred milliseconds or less seems to be sufficient to perform tasks such as genre classification Gjerdingen and Perrott, ; Mace et al.

    More specifically, Gjerdingen and Perrott played participants audio excerpts of commercially available music at different lengths and asked them to indicate the genre of each excerpt. Results by Schellenberg et al. Mace et al. At this timescale there are few, if any discernible melodic, rhythmic, harmonic or metric relationships to base judgements on. Though when musical-structural information is minimal, timbral information can still be rich.

    Timbre is here understood as an umbrella term that denotes the bundle of auditory features other than pitch, loudness, duration that contribute to both sound source categories and sound quality McAdams, In fact, timbre seems to be processed even from very short stimulus durations.

    For instance, Bigand et al. More recent results by Suied et al. In the latter study, performance increased monotonically with the length of the excerpts and plateaued at around 64 ms. Building on this research, Musil et al. The sound similarity test was designed to assess the ability to decode and compare complex musical sound textures and to be independent of temporal processing and memory capabilities and therefore only makes use of very short musical stimuli.

    On the contrary, there is a rich literature on audio features associated with computer-based instrument identification Joder et al. Audio features are most commonly derived from the Short-Time Fourier Transform of the music signal, from which spectral or temporal statistics are computed. A standard example are summary statistics such as the mean i. It is important to note that the utility of specific timbre descriptors as well as the size of feature sets varies considerably across computational and perceptual tasks.

    In effect, timbre description in psychology traditionally employs a handful of, say, less than 10 features, whereas many music information retrieval approaches rely on audio representations with a substantially higher dimensionality Siedenburg et al.

    None of the psychological studies on short audio clips has used audio features to quantitatively model human perceptual responses to very short audio clips. For that reason, it is currently unclear to which extent simple categorization judgements can be predicted by low-level properties of the audio signal, as opposed to higher level concepts such as genre potentially inferred from the audio. But constructing a cognitively adequate model of audio similarity is not only useful for understanding what features and cues listeners extract and process from short audio clips.

    It can also serve as a first step for constructing future adaptive versions of individual differences tests of audio classifications that could allow a systematic scaling of difficulty of sets of audio clips by selecting clips that are more or less similar. This paper aims to contribute toward the understanding of perceptual judgements of similarity for short music clips via a modeling approach. The present contribution is the first study to systematically quantify the extent to which similarity data of short musical excerpts can be explained by acoustic timbre descriptors.

    A notable feature of the current approach is that we not only evaluate the constructed statistical models in terms of their accuracy in describing a given set of observations, but also in their capacities to generalize to unseen data sets. The predictive accuracy of low-level timbre features is further compared with variables that encode meta information in the form of the genre and release date of songs. This manuscript is organized as follows. In Section 2, we describe the experimental samples, stimuli, and procedures that provide the basis for our modeling study.

    In Section 3, the structure of the model is described in detail, in particular with regards to the audio features, normalization schemes, and statistical models of perceptual similarity.

    In Section 4, the presented models are comprehensively evaluated, before potential implications on timbre modeling are discussed in Section 5. This study uses data from two separate experiments that used a sorting paradigm to assess the perceptual similarity of short music clips. Only the data gathered via the similarity sorting paradigm is reported in this paper and has not been reported previously. The Ethics Board of Goldsmiths, University of London approved the research undertaken and reported in the manuscript.

    In the training sample, Participants were mainly UK residents Only 1. Participants in Sample I were tested with ms excerpts. Sample II comprised responses from participants, collected via several experimental batteries that were run at Goldsmiths University of London between and , all of which contained the sound similarity test using ms excerpts.

    Participants came from a young student population undergraduates as well as postgraduates and were less diverse in terms of their educational and occupational background than participants in Sample I 1.

    Prototypical but less well-known songs from four different genres were selected as experimental stimuli, as described by Musil et al. Additionally, following Krumhansl's finding that the approximate recording date of a song can be identified fairly accurately from short excerpts, specific decades were selected for each genre: —70s for jazz, —80s for rock, — for pop and hiphop.

    Exemplary songs for each of these genres were selected from the suggestions of prototypical songs given on the encyclopedic music datbase allmusic. In order to avoid the recognition of specific overly well-known tunes, songs were only selected if they were not present in the all-time top Billboard charts and had never reached the top rank on the UK Billboard charts. Hence, we cannot rule out the possibility that individual participants might have recognized the songs of individual excerpts.

    Aiming for representative sound fragments, excerpts from each song were chosen such that the excerpt did not contain any human voice, there were at least two recognizable notes in the excerpt, and the fragment represented as much a possible the maximal timbral diversity i.

    In addition, the excerpt was preferably taken from a repeated section of the song. A table with all song titles, artists, and the corresponding genre is given in Table 1 of the Appendix Supplementary Materials. Excerpts were extracted directly from.

    For the computation of audio features, all clips were converted to mono by summing both stereo channels. For the two experiments, excerpts of lengths ms Sample I and ms Sample II were used, extracted from different locations in the song, to which a 20 ms fade-in and fade-out was added. In the absence of a perceptual-computational model of sound similarity at the stage of designing the experimental task, genre was the best proxy available to select groups of songs that would sound similar and at the same time different from other groups of songs, thus allowing to tentatively score the performance on the sorting task of each participant.

    But from the analysis of the behavioral data obtained for the ms excerpt set it became clear that many participants scored close to chance level. After piloting different clip lengths, a duration of ms then seemed to produce a distribution of performance scores that better allowed to characterize inter-individual differences.

    The experimental paradigm was similar to the one used by Gingras et al. The participants' task was to listen to 16 short excerpts and to sort them into four groups of four items each by their similarity in sound. Excerpts were identified by icons on a computer screen, while groups corresponded to boxes. Participants could listen to an excerpt by hovering over its icon, and could move icons around by clicking and dragging.

    Participants were allowed to listen to each clip as many times as they wished and change their sorting solution as often as necessary.

    There was no time constraint for the task and participants submitted their sorting solution when they felt that it could not be amended further. Only the final sorted state was recorded and used for subsequent analysis. Pairwise perceptual similarity was defined as the relative number of times two clips were placed in the same group by participants. This measure is obtained by dividing the absolute number of times two clips were placed in the same group by the respective number of participants in each sample.

    The corresponding distribution of similarities with range between zero and one is shown in Figure 1 left panel. Figure 1. Left panel Distribution of similarity data, here defined as the relative number of shared classifications of two clips.

    Recall from Section 2. In order to rule out potentially confounding effects of demographics on the similarity data, we drew subsamples of Sample I that better matched the demographics of the college-student population of Sample II. Among the , participants, there were 32, participants specifically with age between 18 and 24 years. Note that the diagonal entries of the similarity matrices depicted in Figure 1 two rightmost panels play a distinct role.

    In fact, they derive from representing the data in matrix form and not from participants' direct classifications themselves who only encountered distinct clips. The value of the diagonal entries of the matrix automatically equals one, regardless of participants' responses because every clip trivially shares its own group. However, their inclusion in the model bears the danger of inflating figures of merit such as the coefficient of determination R 2.

    It was premiered on October 25, in Meiningen , Germany. The symphony is scored for two flutes one doubling on piccolo , two oboes , two clarinets , two bassoons , contrabassoon third and fourth movements , four horns , two trumpets , three trombones fourth movement only , timpani , triangle third movement only , and strings.

    The symphony is divided into four movements with the following tempo markings :. This is the only one of Brahms' four symphonies to end in a minor key. A typical performance lasts about 40 minutes. This movement is in sonata form , although it features some unique approaches to development.

    For instance, there is no repeat of the exposition; according to the late Malcolm MacDonald , the music is so "powerfully organic and continuously unfolding" that such a repeat would hinder forward progress. The opening theme is initially serene in character, although its composition in a chain of descending thirds adds a fateful air. Featuring a theme in the Hypophrygian mode, heard at the beginning unaccompanied and at the end with a lush orchestral accompaniment, this movement has a modified sonata form with no development section.

    This movement is the only one with the character of a scherzo to be found in Brahms' symphonies. The sonata form itself is modified further, with a foreshortened recapitulation and with the secondary theme nearly absent in the development and coda. This last movement is notable as a rare example of a symphonic passacaglia , which is similar to a chaconne with the slight difference that the subject can appear in more voices than the bass.

    An analysis of this last movement by Walter Frisch provides yet further interpretation to Brahms' structure of this work, by giving sections sonata form dimensions [ citation needed ]. Arnold Schoenberg , in his essay Brahms the Progressive Brahms is often characterized as an conservative composer , pointed out several thematic relationships in the score, as does Malcolm MacDonald in his biography of the composer. The first half of the chaconne theme is anticipated in the bass during the coda at an important point of the preceding movement; and the first movement's descending thirds, transposed by a fifth, appear in counterpoint during one of the final variations of the chaconne.

    Of special note has been the recording conducted by Carlos Kleiber with the Vienna Philharmonic. Our Expertise.

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  • Pairwise perceptual similarity was defined as the relative number of times two clips were placed in the same group by participants.

    This measure is obtained by dividing the absolute number of times two clips were placed in the same group by the respective number of participants in each sample. The corresponding distribution of similarities with range between zero and one is shown in Figure 1 left panel. Figure 1. Left panel Distribution of similarity data, here defined as the relative number of shared classifications of two clips.

    Recall from Section 2. In order to rule out potentially confounding effects of demographics on the similarity data, we drew subsamples of Sample I that better matched the demographics of the college-student population of Sample II. Among the , participants, there were 32, participants specifically with age between 18 and 24 years.

    Note that the diagonal entries of the similarity matrices depicted in Figure 1 two rightmost panels play a distinct role. In fact, they derive from representing the data in matrix form and not from participants' direct classifications themselves who only encountered distinct clips. The value of the diagonal entries of the matrix automatically equals one, regardless of participants' responses because every clip trivially shares its own group.

    However, their inclusion in the model bears the danger of inflating figures of merit such as the coefficient of determination R 2. Because by simply differentiating identical and non-identical clips with a binary variable, one can readily obtain highly significant fits with the similarity data. For that reason, we took a conservative stance and only considered non-identical pairs for the following modeling, corresponding to the lower triangular dissimilarity matrix without diagonal entries accordingly, the distribution of similarities depicted in the left panel of Figure 1 only represents non-identical pairs.

    Modeling the similarity data comprised three main stages: i feature extraction from the audio clips, ii feature normalization, and the iii modeling of pairwise similarities of features. More specifically, we used two sets of audio features, both of which contained 24 features. Both sets were normalized in five different ways but the normalized features were not pooled. The resulting pairwise distances of clips' audio features were then used as predictor variables in a latent-variable linear regression technique, namely partial least-squares regression PLSR.

    Specifically, PLSR attempts to find the multidimensional direction i. Figure 2 visualizes the three modeling stages. The basic model structure is similar to the timbre dissimilarity model presented by Siedenburg et al. Figure 2. Basic model structure. Note that every feature dimension is processed separately before being joined in the regression model. In addition we also combined both sets to obtain a third feature set iii with 48 features. We used the Timbre Toolbox v1.

    For the current purpose, we selected 24 out of its descriptors. This selection possessed great overlap with the 34 descriptors used in Siedenburg et al. In contrast to the isolated tone case, however, ten of the twelve temporal descriptors were not taken into account for the description of clips, because it could be assumed that measures of attack or release-duration would not differ in any meaningful way across the currently used clips, given that they were extracted from the midsts of songs and contained dense musical textures.

    Spectral shape descriptors were computed from an ERB-spaced Gammatone filterbank decomposition of the signal. Spectral descriptors included the first four moments of the spectral distribution, such as the spectral centroid that has been shown to correlate with perceived brightness McAdams, Additional descriptors of the spectral distribution such as the decrease and flatness were also included, measuring spectral slope with an emphasis on lower frequencies and the peakiness of the spectrum, respectively, but also measures of spectrotemporal variation, relevant to capture spectrotemporal variability the so-called spectral flux McAdams et al.

    We included four descriptors that were based on the time domain representation of the signal: the frequency and amplitude of energy modulation over time, and the median and interquartile range of the zero crossing rate.

    A full list of the descriptors is given in Table 2 in the Appendix Supplementary Materials. As an alternative set of features, we considered the commonly-used Mel-frequency cepstral coefficients MFCCs, Eronen, and their temporal derivatives.

    MFCCs are derived via a discrete cosine transform of the log-transformed power of Mel spectra. MFCCs thus represent the shape of an audio signal's spectral envelope: going up from lower to higher coefficients, MFCCs encode increasingly finer scales of spectral detail.

    MFCCs are standard in various tasks in audio content analysis and music information retrieval and have also been proposed as descriptors for timbre perception see the review in Siedenburg et al.

    These features were provided by the MIRtoolbox v1. In order to regularize the often idiosyncratic distributions of the raw feature values, five normalization schemes were considered:. N1 None i. N2 Range normalization to [0, 1],.

    N3 Z-scores with zero mean and unit standard deviation,. The corpus was obtained by extracting clips from a freely-available audio data set sampled at We selected songs for each of the four meta-genres of the current test set jazz, rock, pop, hiphop , from which we extracted ten ms clips each.

    The resulting 4, clips constituted our corpus. All of the above mentioned features were extracted from each clip of the corpus and used for the corpus-based ranking. Per clip, each feature provided one scalar value. For any pair of clips, feature-wise distances were obtained by taking the absolute difference of the pair's respective feature values. In order to handle collinearity of predictors Peeters et al.

    PLSR is a regression technique that projects the predicted and observed variables onto respective sets of latent variables, such that the resulting variables' mutual covariance is maximized. The decomposition maximizes the covariance of T and U , which yields latent variables that are optimized to capture the linear relation between observations and predictions.

    In order to prevent overfitting of the response variable, the model complexity k can be selected via cross-validation. We used the implementation provided by the plsregress.

    The importance of individual predictors in the PLSR model was assessed by bootstrapping, which eventually allowed us to construct sparse regression models. This process was repeated 1, times. If confidence intervals did not overlap with zero, a predictor's contribution was considered to be significant, and the respective feature was selected for the sparse model.

    The goal of the subsequent model evaluation was to identify from among the three different feature sets and the five different normalization schemes an accurate and robust model of the perceptual similarity data. We place a special focus not only on the question how accurately a statistical model can be fitted to training data, but also on how well the model generalizes to a new set of perceptual data gathered from a different set of audio excerpts.

    This question is addressed by including sparse models in the subsequent evaluations that are known to generalize better to new datasets Friedman et al.

    This means, each model is both fitted and tested on the datasets from Sample I ms clips and Sample II ms clips. This evaluation setup also allows us to investigate the question how well a model describes the data set it was fitted to and to what degree it might be overfitted to the training data. The evaluation proceeds in four steps. We first present results for the three feature sets in combination with all five normalization conditions.

    Secondly, we select a subset of the most relevant features from each model via bootstrapping and recompute the performance of the resulting sparse models. Thirdly, we consider the role of meta information such as genre and the release date of recordings. Finally, we discuss the role of individual acoustic features. Table 1 presents the squared Pearson correlation coefficients R 2 for the three full feature sets and five normalization schemes, corresponding to the proportion of variance shared between the model predictions and empirical observations.

    The results indicate that the perceptual similarities of the ms and ms clips were both predicted with fairly similar accuracy. However, there are obvious differences between model fits derived on the training sets and model generalization to novel test sets, which suggests that all models considered at this point generalize rather poorly to unseen data. Generalization of models based on MFCCs was particularly poor and did not provide a single significant correlation on a novel test set.

    Table 1. In terms of the normalization schemes, models using the test-set-based ranking N4 produced the highest performance values overall. This decrease in model fit may be interpreted as an indicator of model overfitting. Hence, in the next evaluation step we aim to achieve better generalization performance and avoid overfitting by applying feature selection.

    Figure 3. Every individual plot shows the correspondence between model predictions x-axis and empirically observed similarities y-axis. Every panel shows models that were trained to ms panel top row or ms clips panel bottom row , and tested on the same two sets left vs.

    Top left panel shows full feature set; top right: sparse feature selection; bottom left: non-acoustic variables only; bottom right: sparse model together with non-acoustic variables. All models utilize test-ranked features N4. We applied the feature selection approach described in Section 3. New York: Schirmer Books. The Symphony. Brahms: the Four Symphonies. Yale music masterworks.

    Yale University Press. Music Criticisms Eduard Hanslick. Penguin Books. May 30, Symphonies by Johannes Brahms. List of compositions by Johannes Brahms. Johannes Brahms. Double Concerto Piano Concerto No. Cello Sonata No. Ballades, Op. F-A-E Sonata. Portal:Classical music. In this guide I will show you how to setup your Mac or Windows laptop so you can close the lid, push the computer to the side, and focus on the dance floor.

    The NV allows you to close your laptop, and focus on what's important - the crowd, and the music. In the following article we will answer a few questions you may have. Having such a wide array of controllers allow the DJ to put together a combination of hardware to compliment their performance based workflow. The Orbit can be used in almost any digital audio applications. In the article below we will answer your most requested questions.

    This guide covers loading a specific Resolume mapping file into the Numark Orbit and the Resolume Arena software. There are detailed steps to mapping the accelerometer to the desired video or audio effect.

    This guide covers how to send Orbit mapping files to your controller as well as how to use the Orbit as a synthesizer controller in the Ableton software. The Numark Orbit has a few user maintenance modes that may come in handy. Here is a quick list of each mode, what it does, and how to access it.

    Like any precision instrument, the Numark V7 requires proper calibration to ensure accuracy. The following guide will cover the step by step calibration procedure.

    This guide will assist you with correctly installing the unit's hardware driver. This includes drivers, latency, computer connection, software authorization, software mapping, setup with Virtual DJ and Traktor, and more.

    There are few controllers that can claim to play just about anything, but Mixdeck Express is certainly one of them. Mixdeck Express is a full-featured DJ controller. This article walks through some frequently asked questions about the Numark Mixdeck Express.

    With the abilityof controlling computer software, you can utilize a program such as Virtual DJ 8 to build crates, organize your audio and DJ with your Mixdeck Quad.

    This article will walk you through the setup needed in order to get your Mixdeck Quad up and running with Virtual DJ 8. The Numark Mixdeck Quad is an extremely versatile piece of equipment that has many features for in-depth control of Virtual DJ. The mixer section can act as an analog audio mixer and also send MIDI information.

    In this guide, we walk through how to use the mixer area on the Mixdeck Quad to control the associated functions in Virtual DJ. This ability is known as 'cross-play'. Without this ability you could only load tracks to the deck in which the USB drive was inserted, making it difficult to plan out a cohesive, non-stop DJ set. This includes setup, configuration and compatibility with Apple products and DJ software titles.

    This article will help with troubleshooting the more common audio issues of the Numark MixTrack 3. This is a class compliant USB device designed to work seamlessly with your computer's sound card.

    If you are unable to get sound from your computer, this article will walk you through checking the appropriate settings. This article walks through some Frequently Asked Questions about the Mixtrack 3. Numark presents the Mixtrack Platinum DJ controller.

    This latest addition to the Mixtrack Numark family. Leave the day-to-day management to us and have more time for the things you want. You can use that extra time to grow your business and enjoy your family! If you have questions about our Guelph property management services, leasing, tenant placement, repairs, or the Ontario Tenancy Act - rely on our property management services to ensure everything goes smoothly.

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