# Detecting Selected Instruments in the Sound Signal
## 1 Introduction  
Detecting instruments in a signal is often used for indexing databases, annotating songs, and creating applications for musicians and music producers [1]. Detecting and classifying musical instruments in a signal is a non-trivial issue. This is due to the fact that any item that is the source of sound phenomena can be considered a musical instrument. Another important factor complicating the problem is the multitude of types of instruments. Moreover, the sound characteristics of two seemingly different types of instruments can be very similar. It may depend on the manner of articulation or even the degree of tuning [2]. The work aims to create methods for discovering which musical instrument appears in a given sound signal. Depending on one's predispositions and experience in listening to music, a person is able to indicate what instruments were used in a given piece. These predispositions and the musical experience influence the precision with which one can detect the instruments heard. The created methods should, therefore, work similarly to those of an experienced listener in a way that, after the learning process with a selected set of values representing/describing the sound, they will recognize instruments in the musical signal with sufficiently high efficiency. Instrument recognition itself is based primarily on the timbre and tone of the sound. Sound timbre is a phenomenon that allows a person to distinguish what type of instrument the sound comes from, recognize speech, ambient sounds, etc. The sound coming from musical instruments is, in fact, the result of overlapping many simple vibrations with different frequencies and intensities, thus creating a complex, multidimensional phenomenon. The parameter values of these individual component tones determine the timbre of the sound. Determining sound timbre involves analyzing the acoustic spectrum, which can be obtained using the Fourier transform. The term timbre may be related to the resemblance to the sound of a musical instrument (e.g., clarinet or cello), the image of temperature (e.g., warm or cold), shape (e.g., round or flat), or color (e.g., light or dark color) [2, 3]. In the described system, as in other systems related to recognizing sound components and analyzing sound signals, the key element is the appropriate extraction of selected features from the signal [4; 5]. Building a model that detects instruments in an audio signal requires multi-step transformations to extract relevant information from the signal. The most popular and widely used method for mapping a parametric acoustic signal is the Mel-Frequency Cepstral Coefficients (_MFCC_). They were initially used in echo detection in seismic waves [6], but they are also used in speech detection problems [7]. _MFCC_ parameters are also used to determine the genre and timbre of a musical piece [8]. The effectiveness of _MFCC_ has also been confirmed in terms of music recommendations [9; 10; 11]. In this work, the extraction of sound components was also achieved by using _MFCC_ coefficients, which are a mathematical representation of how the human ear perceives music. The role of classifiers was played by artificial dense neural networks, convolutional neural networks, and derivative models--the voting committees based on the above-mentioned ones. There are two main ways to generate the final result of the classification of the created ensemble. One of them is to combine the outputs of individual base models with another dense layer (or layers), which adjusts its weights to the final generation of the classification result in the process of training the entire created assembly. The second method is to attach a voting system to the basic models to determine the classification result based on the outputs of the basic models. Such a voting system does not adjust its weights while training the entire ensemble but generates the result in accordance with the voting rules imposed on it. The remainder of the paper is as follows: Section 1 introduces the undertaken issue.