Auscultation is the most remarkable approach that has been used in diagnosing many cardiovascular diseases for many years. It still plays an important role in the diagnosis of heart disease. Sounds produced by the heart frequently reflect the structural abnormalities of the heart. Physicians use the stethoscope as a common tool to listen to the heart sounds and make a correct diagnosis. Modern stethoscopes are making the auscultation easier to be done. Despite murmurs and tones are easily distinguished, weak murmurs and below audibility threshold easily disappear in background sound. Analysis of heart sounds and extraction of its audio features would be important towards the development of automatic diagnosis systems. Phonocardiogram (PCG) is a diagram of sonic vibration of heart beats. Most researches used PCG as an audio input of system to apply different techniques of digital signal processing [1
]. Based on characteristics of the audio signals, it is possible to apply various signal processing and modeling approaches. Healthy heart sound includes symmetric cycles and pulse values. In contrary, unhealthy heart sounds are commonly disordered by different unexpected frequencies.
Segmentation is a technique for separating cycles and its pulses [2
]. Classification of heart sound is another research area that divides heartbeat sounds in different clusters based on their characteristics [1
]. In the similar study, neural network has been used for classification of different heart sounds such as normal, systolic and diastolic murmurs [6
]. A high performance localization technique of the first heart sound pulse was proposed in [7
]. The localization was performed based on an additional enhancement to improve the accuracy of pulse detection. In our previous study on real-time segmentation [8
], a simple segmentation technique using amplitude reconstruction was proposed which divided the heartbeat sound pulses with a high accuracy. However, the limitation was to lose of low-amplitude harmonics.
Automatic music transcription [9
] is an approach to process the audio signals to extract the pitch levels that can be notated as musical notes and the music. Most researches in automatic music transcription attempted to increase the accuracy of the transcription to cover different frequency levels [9
]. Transcription can be applied on heartbeat sounds in order to represent heartbeat sounds with the music notation. In previous studies [13
], heart sounds were represented with MIDI (Musical Instrument Digital Interface) format. A good performance of transcription was illustrated in those studies. For long duration sampling of the heartbeat sounds and developing a biomedical database, text-based formats (i.e. MIDI) are the suitable mediums to convert and store the biomedical signals. Text-based music information retrieval [16
] allows developing query-based system to highlight various events of heartbeat sounds in particularly. In our previous study [18
], music transcription of heartbeat sounds was performed that demonstrated good accuracy for different heart sound samples. We proposed several preparation techniques for de-noising and cleaning heart sound signals in order to use in real-time systems. The results showed that, heart sounds can be represented as musical notation. Since heart sound signals are in very low-frequency domain [19
], automatic transcription techniques that are used for music transcription are not suitable for this particular application. Therefore, in order to provide a high accuracy transcription, two methods can be used. The first method is to provide an automatic transcription technique with a new configuration to cover very low-frequency spectrum which requires complex algorithms and several modifications. The second method is to transfer the heartbeat sounds to the frequency that is used by music instruments, which allows utilizing the ordinary music processing methods.
In this paper, we propose a frequency shifting (transferring) method to increase the accuracy of the heartbeat sounds transcription. We modify automatic music transcription methods to be used in specific frequency spectrum. The process begins with a frequency estimation technique using Fast Fourier Transform (FFT), a commonly used technique. Heart sounds are divided in several parts with similar size that is called window. Thus, FFT is applied for each window and the estimated frequency is approximated to the nearest pitch number. The main problem in this step is the lower frequency of heart sounds in comparison with music. The proposed shifting method aims to solve the problem with transferring the low-frequency samples to high-frequency notes (music instruments). Moreover, the textual transcription is implemented in two processing methods which are real-time (RT) and non-real-time (NRT). The performance of the transcription is investigated in both methods.