dataesr/scientific-paragraphs-categorization huggingface dataset:
A total of eight items were used to measure the use of BDS teams' skills. These items measured a variety of skills in advanced operational techniques, image-processing for special data, machine learning (e. g. , basket analysis and neural network analysis), web analytics, unstructured data mining (e. g. , text analysis, reviews, and tweeter mining), and relevant programming skills. The construct, which was related to big data-driven (BDD) actions, measured various actions based on insights obtained from big data analytics. A total of nine items were used: 1) implementing automated-inventory management, 2) reacting as suggested by big data analytics (BDA), 3) targeting customer demand, 4) intervening in existing strategies and taking relevant actions as recommended by BDA, 5) using big data analytics for automateddecision making, 6) taking BDD actions, 7) investing in markets based on BDA, 8) understanding competitor strengths by utilising BDA, and 9) prioritising tasks based upon insights provided by BDA.
Other steps included informing the respondents about the anonymity of the survey and avoiding singleinformant bias-we collected data from different managers (production and operational analysts, IT managers and analysts/big data scientists, and business development analysts). From a statistical perspective, Harman's one-factor test was utilized. The analysis produced multiple factors, explaining the greater variance compared to a single factor solution or other combinations. Although all statistical approaches adopted to control for CMV bias have some advantages and disadvantages  (Podsakoff et al. , 2003; Malhotra, Kim and Patil, 2006) , a reasonable proxy was provided by the marker variable technique (the variable was the number of languages respondents knew) proposed by  Lindell and Whitney (2001)  with small correlations.
We have brought such concepts together and have emphasised the analytical skills that are imperative for contemporary business operations, which are being inundated with unstructured datasets. Depending on how BDS teams analyse and utilise the insights produced from big data analytics, these very large datasets can provide better insights  (Chen and Zhang, 2014; Akhtar et al. , 2018). We have further contributed to the multidisciplinary literature on business performance measures by integrating specific and multi-dimensional indicators linked with the data-oriented characteristics of teams and their relevant actions, intersecting with the insights produced form big data analytics. Importantly, we have established links not only between BDS teams' skills and business performance, but also with BDD actions, which play a mediating role  (Brown et al.
, 2011; Akhtar et al. , 2018). Our findings have important implications for RBV scholarship, particularly with regard to the technical-human capital of modern businesses and to the relevant multidisciplinary research. Conventional RBV scholarship-including the RBV of information technologyhas overlooked the cross-disciplinary nature of IT management. Recent scholarship conducted from the RBV perspective has begun to explore big data skills and their impacts on organizational performance  (Schoenherr and Speier-Pero, 2015; Wamba et al. , 2017; Wang and Hajli, 2017) ; however, these studies have focussed on the functional and technical skills related to big data, rather than on the sets of skills possessed by multi-functional teams and on the actions required to leverage such skills. However, functional big data skills (e.
g. , Data Mining and Applications Graduate Certificate, Stanford University), which could help them to be better data science teachers. The relevant free online short courses (e. g. , Coursera and edX) could further help non-technical teachers to build foundations for advanced courses in big data analytics and business intelligence. Most crucially, our research points to the importance of teaching not merely distinct functional skills related to big data, but also of training to work in multi-skilled teams that can combine different sets of skills and ultimately leverage them to create value for the organization.
Limitations and future research Our study is affected by the limitations of survey research in general. The theoretical framework was tested using data from specific industries, and the underlying constructs may behave differently in other ones. Therefore, future research could use data from other industries and settings. Also, big data technology and analytical techniques are subject to rapid change and the timing of our research may have affected findings. Thus, future studies may avail themselves of more advanced big data technologies with different impacts on business performance. Future research would need to focus on unstructured data, how they can help to make automated-decisions and develop evidence-based opportunities for policymaking. Roughly, 90% of big data has been generated in the last few years and their production is increasing exponentially.
Supervised machine learning relies on the accessibility of large datasets of annotated data. This is essential since small datasets generally lead to overfitting when training high-dimensional machine-learning models. Since the manual annotation of such large datasets is a long, tedious and expensive process, another possibility is to artificially increase the size of the dataset. This is known as data augmentation. In this paper we provide an in-depth analysis of two data augmentation methods: sound transformations and sound segmentation. The first transforms a music track to a set of new music tracks by applying processes such as pitch-shifting, time-stretching or filtering. The second one splits a long sound signal into a set of shorter time segments. We study the effect of these two techniques (and the parameters of those) for a genre classification task using public datasets.
Introduction A common task in Music Information Retrieval (MIR) is the prediction of metadata based on the music signal content itself, e. g. in audio classification, musical structure segmentation, tempo prediction, fundamental frequency estimation. Whereas some of the methods are based on known properties which can be directly evaluated using dedicated algorithms, other techniques need a number of annotated examples to enable automatic learning of the discriminant characteristics which help to solve the given problem: this is called supervised training. One of the problems of these approaches, for practical reasons, is that the creation of such a dataset is long and tedious. For example, many audio classification tasks ideally need some hundreds of annotated songs per class to achieve a good estimation.
The use of too small or unrepresentative datasets usually leads to overfitting with prediction methods which have a high level of complexity. In this case, the trained models may focus on sound properties which discriminate the few given examples, but which are irrelevant in a general way. This phenomenon may also appear if all the given examples present a common characteristic which is not representative of real-world examples. For example, this can be met if all the song files of the training set are encoded using the same format (e. g. MP3, and same bitrate and codec). In this case, the prediction of a sound file encoded with a different format may fail. When only few examples can be manually annotated, we investigate the use of Data Augmentation which allows to artificially increase the size of the training dataset.
This paper is organised as follows: Section 2 presents the known task of genre classification (which is the task we use for testing data augmentation). The application of data augmentation to sound signals is detailed in Sec. 3. Mignot, R. , & Peeters, G. (2019). An Analysis of the Effect of Data Augmentation Methods: Experiments for a Musical Genre Classification Task. Transactions of the International Society for Music Information Retrieval, 2(1), pp. 97-110. DOI: https://doi. org/10. 5334/tismir. 26 Section 4 presents the transformation process which is used in the following experiments. The implementation of the experiments is then presented in Sec. 5 (datasets, audio features, classification algorithms). The experiments are detailed in Sec. 6 and the results are discussed. Finally, Section 7 concludes this paper.
, 2001) , Support Vector Machines  (SVM; Boser et al. , 1992) , or k-Nearest Neighbour (k-NN;  Cover and Hart, 1967). During training, the dataset is used to provide a collection of descriptors representing all classes in order to train the models. During testing, the same descriptors and the trained models are used to predict the classes of the test signals. The above description corresponds to the simplest approaches, but other approaches exist. For example, the results of a first stage of classifiers can be fused to feed a final classifier (cf. e. g. Ness et al. , 2009). Also, high-level descriptors relating to the beat, pitch and chord can be added to low-level features. Finally, note that Artificial Neural Networks (shallow or deep) can also be used for classification. In recent years, Deep Learning has also been proposed to simultaneously learn relevant features of spectrograms, and to classify the elements, using Convolutional or Recurrent Neural Network  (CNN or RNN; cf.
More recent works (such as  Jaitly and Hinton, 2013; Kanda et al. , 2013; Ragni et al. , 2014; Cui et al. , 2015)  also used data augmentation for Automatic Speech Recognition. These works mainly take advantage of special transformations for speech such as vocal tract length perturbation. In Music Information Retrieval, data augmentation has been also widely used for some years: for genre classification  (Li and Chan, 2011) , for chord recognition  (Lee and Slaney, 2008; Humphrey and Bello, 2012) , for polyphonic music transcription  (Kirchhoff et al. , 2012)  and in singing voice detection  (Schlüter and Grill, 2015). Moreover dedicated software is proposed by  McFee et al. (2015)  which is tested for instrument recognition, and the Audio Degradation Toolbox has been developed and tested for the robustness of different tasks in MIR  (Mauch and Ewert, 2013).
For example,  Li and Chan (2011)  noticed that the MFCC features are influenced by the fundamental frequencies of instruments. It has also been observed that in the dataset GTZAN of  Tzanetakis and Cook (2002) , there is a strong correlation between the genre (classes) and the musical keys of the songs. Then, the trained models may implicitly focus on the dominant key of the genres, which should be avoided. Finally, using transpositions for the training examples, the algorithm becomes invariant to musical key, and the classification is improved. Finally, we note that even if the term 'Data Augmentation' is mainly associated with data transformations, in this paper we consider it with a more general point of view. In that sense, time splitting/segmentation of sounds is also seen as a data augmentation technique, cf.
Sound segmentation For copyright reasons, many public datasets are made of short sound excerpts, and most of the proposed approaches found in the literature only compute one summarising vector of descriptors per input element (cf. e. g. Fu et al. , 2011). But from a practical point of view, it is no more difficult to annotate full tracks than excerpts, if the annotations do not vary over time. Consequently, if the complete duration of each song is available in the dataset, or at least a long enough excerpt, to duplicate the number of annotated examples a simple idea is to split the input signal into several shorter segments in time. Providing that the model used is not sensitive to the duration and that the classes do not vary over time, the segmentation is class-preserving and this transformation of time can easily increase the total number of examples for the machine learning methods used.
Augmentation for training and testing In the previous parts, data augmentation was mainly presented as a method to increase the training dataset size. As said, the main objective is to avoid overfitting for trainable algorithms, such as: PCA, LDA, GMM, SVM, k-NN. Actually, many of the data augmentation methods presented above can also be applied during testing  (Schlüter and Grill, 2015). If not, a single descriptor vector is computed from the whole original input and classified. If transformations or segmentations are used during testing, all the produced elements (transformed sounds/ segments) are separately classified, and a final aggregation rule makes a unique decision based on the results of all the elements. For example,  Peeters (2007)  proposes two methods for the aggregation of segments: the first is based on a majority voting, named "cumulated histogram", and a second uses the mean of class probability estimates, named "cumulated probability"; we use in this paper the "cumulated probability".
The question of how to find the optimal transformation is a difficult task which is not dealt with here. The chosen configurations are ad-hoc, and better choices could be made depending on the task. In Sections 6. 6 and 6. 7, different transformation configurations are tested and compared. Pitch shifting and time stretching, with preservation of formants and transients. The process is based on the work of  Röbel (2003) ;  Röbel and Rodet (2005) , and is implemented by the software SuperVP. Given a strength γ, the mapping is as follows: the pitch shifting factor s p in semitones and the time stretching scaling s t in percent are randomly chosen on an ellipse of equation: (s p /a p ) 2 + (s t /a t ) 2 = γ 2 , with a p = 4 and a t = 5 the respective maximal changes. For this elementary transformation, β k = 1, cf.
Datasets The public dataset ISMIR Genre 2004 1 is a set of 1442 full song tracks (with an average duration of 4 minutes) split into two subsets: 713 files for training, and 729 files for testing. All the songs are partitioned into 6 unbalanced genre classes which are (the numbers give the number of songs for training/testing): 'world' (106/122), ' electronic' (115/114), ' classical' (320/320), 'metal_punk' (45/45), 'rock_pop' (101/102), 'jazz_blues' (26/26). Most of the experiments of Sec. 6 use ISMIR-2004 because it is a wellknown and public dataset. We also consider the small subset of the FMA dataset, from the Free Music Archive  (Defferrard et al. , 2017). The small subset is made of 8 well balanced classes, each of them made of approximately 1000 songs: 'Hip-Hop', 'Pop', 'Folk', 'Experimental', 'Rock', 'International', 'Electronic', 'Instrumental'.
Note that originally the files of this small subset are 30 second excerpts, but to test the segmentation with full duration, we extracted the 8000 corresponding full tracks from the full FMA dataset. This dataset is used in Sec. 6. 4, which needs more full tracks than ISMIR-2004 has. Finally, the public dataset 1517-Artists is tested in Sec. 6. 9  (Seyerlehner et al. , 2010a). It is initially composed of 3180 full tracks annotated with 19 genres. Nevertheless, since it is used in a cross-dataset context with ISMIR-2004, only the genres 'World', 'Electronic&Dance', 'Classical', 'Alternative&Punk', 'Rock&Pop', 'Jazz' and 'Blues' are extracted, and the last two are merged to make the class 'Jazz&Blues'. Then, the new dataset is composed of 1173 unbalanced files which are split into two subsets for training and testing, with the same class repartition.
This optimisation is done using a grid search and a 5-fold cross validation on the ISMIR-2004 training dataset. Then, using the best σ, the models trained on the whole training dataset are tested on the ISMIR-2004 test dataset. For the other experiments, we fixed σ = 1. features In order to examine whether the results are independent of the classification method, most of the experiments of Sec. 6 are also performed with a different method, presented in Appendix A.
3. 4 (mean of probabilities). Table  2  shows the results in terms of accuracies for genre classification of the ISMIR-2004 dataset. The first entry of the array (rows) is for the configuration of the training, and the second entry (columns) is for the testing step. In this experiment, the σ parameter of the SVM is optimised independently for each cell of the table, as detailed in Sec. 5. 2. Even if the optimisation only uses the ISMIR-2004 training dataset, the vectors used during the training and testing of the cross validation are from the respective evaluated configurations which can be different. Increasing the number of training or testing examples by using shorter segments generally improves accuracy, but for too short segments (15 s) it decreases again. Finally, with a baseline of 81.
Natural vs artificial augmentation One important question is: "is it preferable to increase the training set size by data augmentation or by annotating more examples"? That is the purpose of the experiment of this section. The small FMA dataset contains approximately 8000 full tracks, partitioned into 8 classes. Here, we evaluate the classification using 10-fold cross validation, with an artist filter (each artist is associated exclusively to a fold, with all respective original and transformed segments or tracks). For all the folds, the training sets (originally composed of 7200 files (= 8000/10 × 9)) are down-sampled to 1000 in a first case (simulating a small dataset) and to 5000 files in a second case (simulating a larger dataset). In both cases, we test: no augmentation, segmentation with 80 s segments, and transformations with 4 additional transformed sounds.
9% whereas the smaller data set with segmentation or transformation, also producing 5000 vectors, gives 48. 6% or 48. 5% respectively. Thus, more annotations produce a wider variety of examples which improves the learned models. Nevertheless, when it is not possible to manually annotate more songs, data augmentation remains a good alternative, with significant improvements. Note that the relatively low accuracies obtained in Table  5  are due to the FMA dataset itself. As explained by the authors, many annotations may be inconsistent, which makes the classification more difficult. To compare our implementation of Block Level + SVM, we also tested the classification task of FMA-medium with the same context as the experiments of  Defferrard et al. (2017) , i. e. without data augmentation. We obtained an accuracy of 63.
Conversely, Figure  2  shows that the addition of Gaussian noise during the training provides only little benefit when classifying clean sounds. But, observing the column (D) of Figure  3 , the model seems to be very sensitive to Gaussian noise (upper cell), and using it during the training obviously improves the robustness of the classification. From these observations, we can draw the following hypotheses: first, data augmentation can be used to increase the accuracy when we only have a small training dataset, and transformations to which the model is not sensitive are useful (time stretching for example). Second, data augmentation improves the robustness when classifying degraded sounds, and in this case it is useful to use transformations to which the model is sensitive (Gaussian noise for example).
Testing transformations for cross-dataset issues In many cases, the musical tracks collected to make a dataset share common characteristics, such as mixing style or audio compression format. After training a model with the training set of a given dataset, we usually obtain best results using a testing set from the same dataset rather than from another one, possibly with different characteristics. In this present section, we study a cross-dataset scenario, and by using audio transformation during training, we test whether it results in an improvement in robustness. The tested datasets are: ISMIR-2004 (already split into training and testing sets), and 1517-Artists with the class modifications detailed in Sec. 5. 1 which aims to match the taxonomy of  ISMIR-2004. Recall that 1517-Artists has been split like ISMIR-2004 into two subsets for training and testing.
The transformations (if used) are performed with the same chain as in Sections 6. 2, 6. 3 and 6. 4, and segmentation uses segments of 80 s for training and testing. Finally, the classification method is the same: Block-Level features, normalisation, PCA and SVM classifier. The results are presented in Table  7. First, when the testing set and training set come from the same dataset, the results of the modified 1517-Artists show lower accuracy than with ISMIR-2004, which suggests a weaker consistency of its annotations. Even if the use of transformations does not change the accuracy of the tests of ISMIR-2004 (in all the cases), the transformations improve the predictions of the 1517-Artists testset in a cross-dataset context (trained with ISMIR-2004), with a gain of almost 6pp. These results are not as convincing as we expected, but at least they emphasize the improved robustness resulting from data augmentation.
Conclusion In this paper we provide an in-depth analysis of two data augmentation methods: sound transformation and sound segmentation. Testing them for a genre classification task, some experiments have shown their ability to significantly improve the results for small datasets when it is not possible to annotate more examples manually. First, among the tested segmentation configurations of Sec. 6. 1, it was seen that it is preferable to use segments of 30 seconds both during training and testing, rather than a longer duration. But as noted, depending on the descriptors used, segments shorter than that may not provide meaningful representations, and they did not provide improvements with the method tested. Second, the evaluation results of Sec. 6. 2 showed the benefit of applying transformations to the training examples.
For example the best result is also obtained with the highest number of transformations in both the training step and the testing step. Nevertheless, the results of the first column (transformations during the training step and not during the testing step) do not show any significant improvement compared to the reference value of 83%. Note that the mean of the baseline (without data augmentation) is higher with this method than using the method Block Level+SVM. It is now between 83% and 83. 9% (cf. Tables 8-10) instead of 81%. This can be explained by the lower complexity of the method which results in a better behaviour with small datasets. But when using data augmentation, the previous method usually provides better results. As in Sec. 6. 1, Table  9  globally presents better results when using a relatively high number of segments during the two steps, excepted with too short segments (15 s).
But the first column does not show any improvement when classifying original (i. e. non-transformed) signals. Compared to Table  7 , Table  13  presents an interesting, but unexplained, difference: in a cross-dataset scenario, the transformation of the training dataset provides a significant benefit when classifying 1517-Artists tracks using models trained on ISMIR-2004 with the Block Level+SVM method (Sec. 6. 9), but on the contrary with the StdDesc+ModSpec+GMM method, the benefits are present when classifying ISMIR-2004 tracks using models trained on 1517-Artists.
Testing of transformation overfitting This section tests the classification of transformed signals when the models are trained with different transformations (individual or chains). The aim is to demonstrate that the training with one individual transformation produces overfitting to the transformation of the training. Using 13 different transformations, Figure 3 compares the classi fication of original and transformed signals when the training is performed with original or transformed datasets. Note that the σ parameter is optimised once for each row of Figure3, and 25 repetitions are performed for each cell. For sake of simplicity, only the mean accuracy is displayed.