Now that we understood how we can play around with audio data and extract important features using python. In the following section, we are going to use these features and build a ANN model for music genre classification. Tzanetakis and P. The dataset consists of audio tracks each 30 seconds long.

It contains 10 genres, each represented by tracks. The tracks are all Hz monophonic bit audio files in. The dataset can be download from marsyas website. First of all, we need to convert the audio files into PNG format images spectrograms.

From these spectrograms, we have to extract meaningful features, i. Once the features have been extracted, they can be appended into a CSV file so that ANN can be used for classification. Now convert the audio data files into PNG format images or basically extracting the Spectrogram for every Audio. Data preprocessing: It involves loading CSV data, label encoding, feature scaling and data split into training and test set. Well, part 1 ends here.

In this article, we did a pretty good analysis of audio data. We understood how to extract important features and also implemented Artificial Neural Networks ANN to classify the music genre. In part 2, we are going to do the same using Convolutional Neural Networks directly on the Spectrogram.

I hope you guys have enjoyed reading it. He has over 4 years of working experience in various sectors like Telecom, Analytics, Sales, Data Science having specialisation in various Big data components. Reposted with permission. By subscribing you accept KDnuggets Privacy Policy. A sound wave, in red, represented digitally, in blue after sampling and 4-bit quantisation , with the resulting array shown on the right.

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In this example, integers were cast to floating point. If I cast some floating point numbers to be of integer dtype, the decimal part will be truncated:. Should you have an array of strings representing numbers, you can use astype to convert them to numeric form:. If casting were to fail for some reason like a string that cannot be converted to float64 , a TypeError will be raised.

See that I was a bit lazy and wrote float instead of np. Calling astype always creates a new array a copy of the data , even if the new dtype is the same as the old dtype. In complex computations, you may accrue some floating point error , making comparisons only valid up to a certain number of decimal places.

Arrays are important because they enable you to express batch operations on data without writing any for loops. This is usually called vectorization. Any arithmetic operations between equal-size arrays applies the operation elementwise:. Arithmetic operations with scalars are as you would expect, propagating the value to each element:.

Operations between differently sized arrays is called broadcasting and will be discussed in more detail in Chapter Having a deep understanding of broadcasting is not necessary for most of this book. NumPy array indexing is a rich topic, as there are many ways you may want to select a subset of your data or individual elements.

One-dimensional arrays are simple; on the surface they act similarly to Python lists:. An important first distinction from lists is that array slices are views on the original array. This means that the data is not copied, and any modifications to the view will be reflected in the source array:.

If you are new to NumPy, you might be surprised by this, especially if you have used other array programming languages which copy data more zealously. As NumPy has been designed with large data use cases in mind, you could imagine performance and memory problems if NumPy insisted on copying data left and right.

If you want a copy of a slice of an ndarray instead of a view, you will need to explicitly copy the array; for example arr[]. With higher dimensional arrays, you have many more options. In a two-dimensional array, the elements at each index are no longer scalars but rather one-dimensional arrays:. Thus, individual elements can be accessed recursively. But that is a bit too much work, so you can pass a comma-separated list of indices to select individual elements.

So these are equivalent:. See Figure for an illustration of indexing on a 2D array. In multidimensional arrays, if you omit later indices, the returned object will be a lower-dimensional ndarray consisting of all the data along the higher dimensions.

Similarly, arr3d[1, 0] gives you all of the values whose indices start with 1, 0 , forming a 1-dimensional array:. Note that in all of these cases where subsections of the array have been selected, the returned arrays are views.

Like one-dimensional objects such as Python lists, ndarrays can be sliced using the familiar syntax:. Higher dimensional objects give you more options as you can slice one or more axes and also mix integers. Consider the 2D array above, arr2d. Slicing this array is a bit different:. As you can see, it has sliced along axis 0, the first axis.

A slice, therefore, selects a range of elements along an axis. You can pass multiple slices just like you can pass multiple indexes:. When slicing like this, you always obtain array views of the same number of dimensions.

By mixing integer indexes and slices, you get lower dimensional slices:. See Figure for an illustration. Note that a colon by itself means to take the entire axis, so you can slice only higher dimensional axes by doing:. Of course, assigning to a slice expression assigns to the whole selection:. Suppose each name corresponds to a row in the data array and we wanted to select all the rows with corresponding name 'Bob'.

Thus, comparing names with the string 'Bob' yields a boolean array:. You can even mix and match boolean arrays with slices or integers or sequences of integers, more on this later :. To select everything but 'Bob' , you can either use! Selecting data from an array by boolean indexing always creates a copy of the data, even if the returned array is unchanged. The Python keywords and and or do not work with boolean arrays. Setting values with boolean arrays works in a common-sense way.

To set all of the negative values in data to 0 we need only do:. Setting whole rows or columns using a 1D boolean array is also easy:. Fancy indexing is a term adopted by NumPy to describe indexing using integer arrays. To select out a subset of the rows in a particular order, you can simply pass a list or ndarray of integers specifying the desired order:. Hopefully this code did what you expected! Using negative indices select rows from the end:.

Passing multiple index arrays does something slightly different; it selects a 1D array of elements corresponding to each tuple of indices:.

Take a moment to understand what just happened: the elements 1, 0 , 5, 3 , 7, 1 , and 2, 2 were selected. Here is one way to get that:. Another way is to use the np. Keep in mind that fancy indexing, unlike slicing, always copies the data into a new array. Transposing is a special form of reshaping which similarly returns a view on the underlying data without copying anything. Arrays have the transpose method and also the special T attribute:.

When doing matrix computations, you will do this very often, like for example computing the inner matrix product X T X using np. For higher dimensional arrays, transpose will accept a tuple of axis numbers to permute the axes for extra mind bending :. Simple transposing with. T is just a special case of swapping axes.

A universal function, or ufunc , is a function that performs elementwise operations on data in ndarrays. You can think of them as fast vectorized wrappers for simple functions that take one or more scalar values and produce one or more scalar results.

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