Abstract:
The present invention relates to a method and apparatus for selectively and retroactively recording only a music section out of radio broadcast content. According to the present invention, there is provided a method for selectively and retroactively recording only a music section out of radio broadcast content, comprising the steps of (a) detecting a start point of the music section; (b) temporarily recording the music section from the start point in a buffer memory; (c) detecting a command to record the music section placed by a user; and (d) transferring the music section recorded in the buffer memory to a semi-permanent memory.

Description:
FIELD OF THE INVENTION  
       [0001]     The present invention relates to a digital recorder and a method for automatically selecting and storing music from radio broadcasting contents, and more particularly, to a digital recorder and a method for automatically extracting only music section from radio broadcasting contents and storing the selected music from beginning to end according to a user&#39;s recording selection.  
       DESCRIPTION OF THE PRIOR ART  
       [0002]     Recently, people who enjoy listening to music prefer to use digital recorders, which can reproduce a high quality of musical sound, rather than conventional analog recorders. As a device for reproducing a digital music file, a digital recorder is relatively small in size, because it contains a nonvolatile digital memory (media card) capable of reading and writing music data. Due to such an advantage, portable digital recorders, so-called “MP3 (MPEG Audio-Layer 3) players,” have rapidly become popular. Generally, MP3 players not only reproduce stored music data but also have a radio function to receive live FM radio music broadcasts.  
         [0003]      FIG. 1  is a block diagram showing the configuration of a conventional MP3 player having a radio function.  
         [0004]     The conventional MP3 player  100  comprises an antenna  110 , a tuner  120 , a sound output section  130 , a DSP (digital signal processor)  140 , an external device connecting section  150 , a controller  160 , a music data storing section  170 , a display section  180  and a key operating section  190 .  
         [0005]     The antenna  110  receives sky-wave signals. The tuner  120  receives and outputs a radio signal corresponding to a tuned channel, among sky-wave signals received by the antenna  110 . The sound output section  130  filters and amplifies an analog acoustic signal received from the tuner  120  in order to output the signal as an audible sound. The DSP  140  converts an analog acoustic signal received from the tuner  120  into digital data or digital music data into an analog acoustic signal, and outputs the converted signal or data. Also, the DSP  140  decodes and converts encoded music data into an analog acoustic signal and outputs the signal. The external device connecting section  150  is connected to an external device (e.g., a computer) in order to download MP3 music data. The controller  160  controls the storage and output of MP3 music data, as well as the receiving and output of a radio broadcasting signal. The music data storing section  170  is a storage medium in the form of a flash memory or a hard disk for storing multiple music data compressed in MP3. If the music data storing section  170  has a capacity of 64 Mbytes or 128 Mbytes, it can store about 16 or 32 songs of MP3 music files. The display section  180  displays the operational state of the MP3 player. The key operating section  190  performs an input operation for selecting a radio broadcasting channel or for selecting and outputting a MP3 music file.  
         [0006]     If a user wishes to listen to music through the MP3 player  100 , he or she can select a radio function to listen to music in real time in a desired music broadcasting channel. Alternatively, the user can select music data stored in the music data storing section  170  to listen to desired music.  
         [0007]     Particularly, while listing to an FM radio music broadcast by selecting the radio function, the user can record the music, which is being currently broadcasted on radio, by pressing a record button (not shown) provided in the key operating section  190 . Then, the controller  160  controls the DSP  130  to convert a music signal outputted from the tuner  120  into digital data, and stores the digital data in the music data storing section  170 . If the user presses the record button again when the music ends, the recording operation will be stopped. The user should pay close attention to correctly recognize the beginning and end of the music.  
         [0008]     If a radio channel streams music after an introduction to the music, users will have time to prepare before recording the music. However, in most cases, users decide to record music after hearing the beginning of the music on the radio. In other words, live music received from a radio station, excluding the beginning part thereof, can be stored in the music data storing section  107 . When reproducing the music after completion of the recording operation, the users can only hear the part recorded after some lapse of time. Therefore, in conventional MP3 players  100 , an additional function has been demanded to record and reproduce music broadcasted on radio from the beginning thereof, even in a case in which a user starts to record the music after some lapse of time.  
       SUMMARY OF THE INVENTION  
       [0009]     Accordingly, the present invention has been made to solve the above-mentioned problems occurring in the prior art, and an object of the present invention is to provide a digital recorder and a method for automatically selecting music from radio broadcasting contents to enable a user to record and reproduce music broadcasted on radio from the beginning thereof at any time according to the user&#39;s selection.  
         [0010]     In order to accomplish this object, there is provided a digital recorder which selects a music signal from broadcasting signals and store the selected signal as music data, and which includes a tuner for receiving and selecting broadcasting signals, a sound output section for outputting a selected broadcasting signal as an audible sound, a music data storing section comprising a temporary storage area for temporarily storing music data and a permanent storage area for storing music data permanently or for a long-term, and a display section for displaying the operational state of the digital recorder, improvements of which comprise: a signal processing section for converting a broadcasting signal into digital data or digital data into an analog signal, compressing and encoding digital data into music data, or decoding and outputting compressed digital data; a music extracting section for dividing digital data outputted from the signal processing section into music data and non-music data according to a music extracting algorithm to extract only the music data, and generating and outputting beginning/end data for recognizing the beginning and end of the extracted music data; a key input section provided with a broadcast key for converting the operation mode of the digital recorder into a radio broadcast receiving mode and a record key for implementing a function to record and store a music signal broadcasted on radio; and a microprocessor for controlling the signal processing section to temporarily store only the music data extracted by the music extracting section in the temporary storage area of the music data storing section, transferring the music data temporarily stored in the temporary storage area to the definite storage area when the record key is pressed, and definitely storing and maintaining the music data in the definite storage area.  
         [0011]     In order to accomplish the above object, there is also provided a method for selectively storing music using a digital recorder comprising: a tuner for receiving and selecting a broadcasting signal; a sound output section for outputting a selected broadcasting signal as an audible sound; a digital signal processor (DSP) for converting a broadcasting signal into digital data or digital data into an analog signal, compressing and encoding digital data into music data, or decoding and outputting compressed digital data; a music extracting section for extracting only music data from the digital data received from the DSP; a music data storing section for storing music data; a display section for displaying the operational state of the digital recorder; and a key input section for converting the operation mode of the digital recorder into a radio broadcast receiving mode and inputting a command to implement the recording of a music signal broadcasted on radio, said method comprising the steps of: (a) said tuner&#39;s outputting a broadcasting signal to the sound output section and sending the signal to the DSP; (b) said DSP&#39;s converting the broadcasting signal into digital data and outputting the data to the music extracting section; (c) said music extracting section&#39;s extracting music data from the digital data according to a music extracting algorithm; (d) recognizing the beginning and end of the extracted music data and temporarily storing the data in the music data storing section; (e) determining whether a command to record music, which is being currently outputted to the sound output section, is inputted from the key input section; and (f) definitely storing and maintaining the music data which is temporarily stored in the music data storing section. 
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0012]     The above and other objects, features and advantages of the present invention will be more apparent from the following detailed description taken in conjunction with the accompanying drawings, in which:  
         [0013]      FIG. 1  is a block diagram showing the configuration of a conventional MP3 player having a radio function;  
         [0014]      FIG. 2  is a block diagram showing the configuration of a digital recorder for selectively storing music according to the present invention;  
         [0015]      FIG. 3  is a block diagram showing the inner configuration of a music extracting section comprising an artificial neural network according to a first embodiment of the present invention;  
         [0016]      FIG. 4  is a flow chart showing a process of automatically selecting and storing music using an artificial neural network according to the first embodiment of the present invention;  
         [0017]      FIG. 5  is a block diagram showing the inner configuration of a music extracting section utilizing a frequency analysis according to a second embodiment of the present invention;  
         [0018]      FIG. 6  shows the constituents of a music signal, including a mute;  
         [0019]      FIG. 7  is a flow chart showing a process of automatically selecting and storing music using a frequency analysis according to the second embodiment of the present invention;  
         [0020]      FIG. 8  is a block diagram showing the inner configuration of a music extracting section utilizing an HMM (hidden Markov model) according to a third embodiment of the present invention;  
         [0021]      FIG. 9  shows the principle of Viterbi algorithm for finding the most likely state sequence with the maximum probability; and  
         [0022]      FIG. 10  is a flow chart showing a process of automatically selecting and storing music utilizing an HMM according to the third embodiment of the present invention. 
     
    
     DETAILED DESCRIPTION OF THE INVENTION  
       [0023]     Hereinafter, preferred embodiments of the present invention will be described with reference to the accompanying drawings. In the following description and drawings, the same reference numerals are used to designate the same or similar components. Therefore, repetition of the description on the same or similar components will be omitted.  
         [0024]      FIG. 2  is a block diagram showing the configuration of a digital recorder for selectively storing music according to the preferred embodiments of the present inventions.  
         [0025]     Referring to  FIG. 2 , the digital recorder  200  comprises a DSP  210 , a music extracting section  220 , a key input section  230 , a microprocessor  240  and a program memory  250 .  
         [0026]     The DSP  210  includes: an ADC (analog to digital converter)  211  for converting an analog signal into a digital signal; a DSP core  212  for controlling the overall operation of the DSP  210 ; a DAC (digital to analog converter)  213  for converting a digital signal into an analog signal; an encoder  214  for compressing and encoding an analog signal, for example, into MP3 file data; a DSP program section  215  storing a program for converting a broadcasting signal received from a tuner  120  into digital data according to a control command from the microprocessor  240 , compressing and encoding the digital data, and decoding and outputting the compressed digital data; and a decoder  216  for decoding the compressed digital data. Of course, the digital recorder can include a hardware-based signal processing section, instead of the DSP  210 .  
         [0027]     The music extracting section  220  divides a digital signal received from the DSP  210  into music data and non-music data according to its own music extracting algorithm in order to extract the music data, while removing the non-music data. To perform this extracting function, the music extracting section  220  utilizes an artificial neural network, a frequency analysis or an HMM (hidden Markov model).  
         [0028]     The key input section  230  includes a broadcast key  232  for converting the operation mode of the digital recorder into a radio broadcast receiving mode and a record key  234  for implementing a function to record and store a music signal which is being broadcasted on radio, as well as a channel key for selecting a channel and a volume key for adjusting the volume of an acoustic output.  
         [0029]     When the digital recorder is in a broadcast receiving mode, the DSP  210  and the music extracting section  220  divide broadcasting signals received by the tuner  120  into music data and non-music data to extract only the music data. The music data is temporarily stored in the music data storing section  170 . When the record key  234  provided in the key input section  230  is pressed, the music data currently being outputted and temporarily stored is definitely stored from the beginning thereof in the music data storing section  170 . The microprocessor  240  controls the overall process of storing the music data.  
         [0030]     The music data storing section  170  has a temporary storage area for temporarily storing music data and a definite storage area for definitely storing music data according to a command to definitely record and store the music data. The temporary storage area can store music data of an amount close to one song. When the record key  234  is pressed for a particular music, the microprocessor  240  transfers the music data stored in the temporary storage area to the definite storage area in order to definitely store the music data.  
         [0031]      FIG. 3  is a block diagram showing the inner configuration of the music extracting section  220  including an artificial neural network according to the first embodiment of the present invention.  
         [0032]     The music extracting section  220  according to the first embodiment extracts only music data from broadcasting signals received at the currently tuned channel according to a music extracting algorithm utilizing an artificial neural network. When large amounts of acoustic signals included in broadcasting signals are inputted, the music extracting algorithm utilizing an artificial neural network implements an operation on the inputted signals. The music extracting algorithm reduces the dimension of input data to divide them into music signals and non-music signals, and removes the non-music signals to output only the music signals.  
         [0033]     To improve understanding of the first embodiment of the present invention, “artificial neural networks” will be explained in more detail.  
         [0034]     The “artificial neural networks” are computation systems modeled after the structure of the human or animal brain. Neurons in the brain, being in highly complex connections, interact with each other to process information in a parallel and distributed fashion. The artificial neural networks are patterned after biological neurons. Every artificial neural network forms a neural network using threshold logic units having critical values and applies a learning algorithm for adapting the given neural network to the environment such as data.  
         [0035]     Various neural network models are available according to the architectures of forming neural networks. The most generally used model is a multilayer perceptron architecture, wherein neurons are grouped into layers, including a layer of input neurons, a layer of output neurons and an intermediate layer of hidden neurons (or hidden nodes) as shown in  FIG. 3 . While there is no link between neurons on the same layer, each neuron on a layer other than the output layer is connected to every neuron on the next layer. The neurons on the first layer send their output in the direction of the neurons on the second layer, which is termed “feed-forward.” A weight Wmh is given on each connection between neurons, and a weighed input is summed up at the next layer. The neural network learns to recognize the weight. As a weight learning algorithm, “error backpropagation” is generally adopted. In the present invention, the multilayer perceptron architecture is used as an artificial neural network. Also, such a single hidden layer, feed-forward neural network and error backpropagation learning algorithm are used in the present invention.  
         [0036]     According to the first embodiment of the present invention, the music extracting section  220  utilizes an artificial neural network trained with patterns of frequencies and having the multilayer perceptron architecture. It is important to appropriately adjust training parameters, such as epoch (one pass over all patterns in the training set) and the number of hidden nodes, when training the neural network. The music extracting section  220  divides broadcasting signals into music signals and non-music signals to extract the music signals only, while removing the non-music signals.  
         [0037]     Hereinafter, the operation of the digital recorder, which extracts music data using an artificial neural network, will be explained in further detail with reference to  FIG. 4 .  
         [0038]      FIG. 4  is a flow chart showing a process of automatically selecting and storing music using an artificial neural network according to the first embodiment of the present invention.  
         [0039]     When the digital recorder  200  is powered and the microprocessor  240  is in a waiting mode for controlling the overall operation of the recorder according to a key input at the key input section  230  (S 402 ), a user can press the broadcast key  232  provided in the key input section  230  to listen to the radio. When the broadcast key  232  is pressed (S 404 ), the microprocessor  240  controls the tuner  120  to receive broadcasting signals of a currently tuned channel. The microprocessor  240  also controls the DSP  210  to encode the received broadcasting signals and converts them into digital data. Of course, the user can select another channel by operating the channel key provided in the key input section  230 . The microprocessor  240  remembers the channel tuned by the key input section  230 . Unless the user selects another channel using the key input section  230 , the microprocessor  240  controls the tuner  120  to receive the broadcasting signals of the tuned channel. If the user selects another channel, the microprocessor  240  will then control the tuner  120  to receive broadcasting signals of the other channel (S 406 ).  
         [0040]     The broadcasting signals are received by the tuner  120 . The tuner  120  outputs the broadcasting signals of the tuned channel to the sound output section  130  and to the DSP  210  simultaneously. The sound output section  130  outputs the analog broadcasting signals received from the tuner  120  as an audible sound. The DSP core  212  of the DSP  210  converts the broadcasting signals received from the tuner  120  into digital data using the ADC  211 . Also, the encoder  214  encodes the digital data to music file data and temporarily stores the data in the music data storing section  170 . While the user is listening to the voice and music broadcasted over the radio, the digital recorder  200  extracts only music signals from the broadcasting signals and temporarily stores the extracted music signals. If the user inputs a command to record music, the digital recorder  200  definitely stores the music which is being currently broadcasted on radio.  
         [0041]     Broadcasting signals received by the digital recorder  200  have various segments, such as a music segment for broadcasting music, a commercial break segment for commercial messages and a speech segment for transferring the voice of a radio DJ (disk jockey) or a radio cast. The broadcasting signals received by the antenna  110  are transmitted to the tuner  120 . The tuner  120  outputs the broadcasting signals of the currently tuned channel to the DSP  210  (S 408 ). The DSP  210  outputs the broadcasting signals to the sound output section  130  via the ADC  211 , the DSP core  212  and the DAC  213 . At the same time, the DSP  210  encodes music signals included in the broadcasting signals into digital music data, for example, MP3 music data, using the encoder  214  and outputs the encoded data to the music extracting section  220  (S 410 ).  
         [0042]     As shown in  FIG. 3 , the music extracting section  220  receives the broadcasting signals outputted from the DSP  210  as an input, and divides the signals into music data and non-music data according to a predetermined music extracting algorithm using an artificial neural network. The music extracting section  220  removes the non-music data and temporarily stores only the music data in the music data storing section (S 412 ). The microprocessor  240  controls the DSP  210  to store music, which is being currently outputted to the sound output section  130 , in the temporary storage area of the music data storing section  170 . When a record command is inputted from the key input section  230 , the microprocessor  240  controls the DSP  210  to store and maintain the music data, which is temporarily stored in the music data storing section  170 , retroactively from the beginning of the music data.  
         [0043]     If the user wishes to record music which is being currently outputted to the sound output section  130 , he or she should press the record key  234  of the key input section  230 . When the record key  234  is pressed (S 414 ), the microprocessor  240  controls the DSP  140  to transfer the music data, which is temporarily stored in the temporary storage area of the music data storing section  170 , to the definite storage area in order to definitely store and maintain the music data (S 416 ).  
         [0044]     The music data storing section  170  stores music data in the order they are received. If the record key  234  is not pressed, music data will be continuously stored in the music data storing section  170  by the music extracting section  220 . If the music data exceed the storage capacity of the music data storing section  170  (that is, if new music data is received to be stored in the full music data storing section  170 ), the DSP  210  will delete the music data one by one in the order they were stored, in order to store the new music data.  
         [0045]     The key input section  230  includes a key with a function to delete music data. The key input section  230  outputs a list of the music data stored in the music data storing section  170  to the display section  180 . The user can delete any selected music data by pressing the delete key.  
         [0046]     According to the first embodiment of the present invention, the digital recorder  200  can output received broadcasting signals as an audible sound. Also, the digital recorder  200  can select only music signals from the received broadcasting signals and store the music signals as digital music data.  
         [0047]      FIG. 5  is a block diagram showing the inner configuration of a music extracting section  500  utilizing a frequency analysis according to the second embodiment of the present invention.  
         [0048]     Generally, radio is broadcasted in either monophonic (mono) or stereophonic (stereo) sound.  
         [0049]     The mono mode is to broadcast acoustic signals using a single frequency channel. Since the mono mode outputs sound received by a sound receiving means disposed at a place regardless of the sound source, the acoustic signals outputted through a mono audio system may be slightly different from the original acoustic signals. By contrast, the stereo mode is to broadcast acoustic signals using a plurality of frequency bandwidths. The stereo mode divides an acoustic signal into a left stereo signal and a right stereo signal according to the sound source, and transfers each of the left and right stereo signals to a plurality of frequency bandwidths. When compared to the mono mode, the stereo mode gives greater realism because it outputs acoustic signals which are closer to the original sound.  
         [0050]     Sounds broadcasted by radio are generally classified into four segments, i.e., a radio cast&#39;s speech segment, a music and cast&#39;s speech coexisting segment, a commercial break segment and a music segment. The speech segment is closer to mono signals, while the other segments are closer to stereo signals. A stereo broadcasting signal has a slight difference between the information of the left channel and that of the right channel. The phase values of the sound waveforms in the two channels with lapse of time can be compared to each other in order to determine whether the phase values of the two channels are identical. If there is no phase difference, the broadcasting signal will be determined to be monophonic. If monophonic speech signals are removed, it will be possible to obtain music signals which are mostly stereo signals.  
         [0051]     Referring to  FIG. 5 , the music extracting section  500  according to the second embodiment of the present invention analyzes broadcasting signals and divides them into mono signals and stereo signals. The music extracting section  500  removes the mono signals to obtain the stereo signals only. In other words, broadcasting signals including mono signals are shown on the time axis. A volume difference between the left and right channels of the broadcasting signals is calculated on the time axis. When the volume difference is near zero, the broadcasting signals are determined to be monophonic. When a volume difference greater than any critical value lasts for a certain period of time, the signals are determined to be stereophonic. Accordingly, the mono signals are removed to obtain the stereo signals only.  
         [0052]     The music extracting section  500 , which utilizes a frequency analysis according to the second embodiment of the present invention, includes an acoustic data operator section  510 , a non-music removing section  520 , a music beginning/end determining section  530  an a spectrum analysis section  540 .  
         [0053]     The acoustic data operator section  510  implements operations on the left channel data and right channel data of the broadcasting data received from the DSP  210  and outputs data on the operation results. When the results are near zero, the broadcasting data are determined to be mono data. When the results show that a value greater than a critical value lasts for a certain period of time, the broadcasting data are determined to be stereo data. Based on the operation results, the mono data is removed to obtain only the stereo data.  
         [0054]     The music beginning/end determining section  530  outputs the music data received from the non-music removing section  520  to the DSP  210 . Also, the music beginning/end determining section  530  generates beginning/end data for discriminating and recognizing the beginning and end points of the music data and transfers the beginning/end data to the microprocessor  240 . For this transfer, a separate output port is provided. In addition, the music beginning/end determining section  530  sends the received music data to the spectrum analysis section  540 , when it fails to discriminate the beginning part of new music data from the end part of previous music data because there is no mute between the two music data or there is an overlapping part between the two music data. The spectrum analysis section  540  performs a spectrum analysis on the music data received from the music beginning/end determining section  530  to discriminate between the beginning and ending g signals of music, and sends beginning/end data for recognizing the beginning and end signals to the microprocessor  240 .  
         [0055]     In order to discriminate between the beginning and end parts of music, the digital recorder  200  of the present invention detects a fade-out at the end part of music data. Most music broadcasted on radio are faded out at their ending parts. According to the second embodiment of the present invention, the music beginning/end determining section  530  of the music extracting section  500  detects the fade-out in each music data, thereby discriminating the beginning of the following music from the end of the previous music.  
         [0056]     As shown in  FIG. 6 , there may be a mute between a previous music signal A and a following music signal B. When there is a mute after output of a music signal A, the music beginning/end determining section  530  determines that the music signal A ends. When a music signal B follows the mute, the music beginning/end determining section  530  determines that the music signal B begins. The music beginning/end determining section  530  generates beginning/end data based on such determination and outputs the data to the microprocessor  240 .  
         [0057]     Generally, a frequency signal has a greater energy value at a point where a speech or music signal is present. On this basis, the music beginning/end determining section  530  calculates an energy variation. The music beginning/end determining section  530  recognizes a lower energy point as a mute or a probable ending point of music. The energy value is obtained by squaring the phase value of the music data in frames, which is received from the non-music removing section  520 , and taking the log of the squared value.  
         [0058]     In most music genres other than classical music, a single music signal has a length of about three to five minutes. When the beginning and end points of music are determined only by the presence of a mute, it is likely that a mute in the middle of music may be erroneously recognized as the beginning or end point of music. In order to reduce the error rate in determining the beginning and end points of music, the music beginning/end determining section  530  detects and determines the beginning and end points of the music, taking into account that the average length of a single music signal is three to five minutes.  
         [0059]     Hereinafter, the operation of the digital recorder, which includes the music extracting section  500  utilizing a frequency analysis, will be explained in further detail with reference to  FIG. 7 .  
         [0060]      FIG. 7  is a flow chart showing a process of selectively storing music utilizing a frequency analysis according to the second embodiment of the present invention.  
         [0061]     The digital recorder  200  has both functions of reproducing stored music data and receiving radio broadcasts in real time. When the user sets the digital recorder  200  in a broadcast receiving mode by pressing the broadcast key  232  provided in the key input section  230 , the microprocessor  240  controls the tuner  120  to receive broadcasting signals at the tuned channel (S 702 ).  
         [0062]     The tuner  120  outputs the broadcasting signals received by the antenna  110  to the sound output section  130  and at the same time sends the broadcasting signals to the DSP  210  (S 704 ) in order to extract music signals from the broadcasting signals in preparation for storing music data, while enabling the user to hear the broadcast. In the DSP  210 , the broadcasting signals are converted into digital data by the ADC  211 . The DSP core  212  divides the digital music data into left channel data and right channel data and sends the divided data to the music extracting section  220 . The left and right channel music data outputted from the DSP  210  are transferred to the acoustic data operator section  510  of the music extracting section  220 . The acoustic data operator section  510  implements an operation on the left channel data and right channel data received from the DSP  210  and outputs the operation results (S 708 ). When the results are near “0”, the data are recognized as mono data. When the results show that a value greater than a critical value lasts for a certain period of time, the data are recognized as stereo data.  
         [0063]     Based on the operation results received from the acoustic data operator section  520 , the non-music removing section  520  removes the mono speech data and outputs only the stereo music data to the music beginning/end determining section  530  (S 710 ). The music beginning/end determining section  530  determines the beginning and end points of the music data received from the non-music removing section  520 , based on (1) the fade-out in the music data, (2) the presence of a mute in the music data, or (3) the average length (3 to 5 minutes) of single music data. (4) When there is an overlapping part between previous music data and following music data, the music beginning/end determining section  530  outputs the music data to the spectrum analysis section  540  to perform a spectrum analysis on the music data and discriminate between the beginning and ending points of music. Lastly, (5) the beginning and end points of music can be determined based on the energy value obtained by squaring the phase value of the music data in frames and taking the log of the squared value. The beginning and end points of music data are determined based on a combination of the five factors or processes. The music beginning/end determining section  530  generates beginning/end data informing the beginning and end points of the music data and transfers the beginning/end data to the microprocessor  240 . The microprocessor  240  stores the beginning/end data in a non-music storage area of the music data storing section  170  (S 712 ). The music beginning/end determining section  530  not only generates the beginning/end data but also outputs the music data to the DSP  210 . The DSP  210  encodes the music data, which is being outputted, and stores it in the temporary storage area of the music data storing section  170  in preparation for recording the music that the user is currently hearing on the radio.  
         [0064]     When the user presses the record key  234  provided in the key input section  230  in order to record the music currently broadcasted on radio (S 714 ), the microprocessor  240  reads the beginning/end data of the music, which is being currently outputted, from the non-music storage area of the music data storing section  170 . Based on this beginning/end data, the microprocessor  240  recognizes the beginning and end of the music data temporarily stored in the temporary storage area of the music data storing section  170   b  and transfers the music data to the definite storage area to definitely store and maintain the music data (S 716 ).  
         [0065]     The temporary storage area of the music data storing section  170  is capable of storing music data amounting to about one song. The temporary storage area temporarily stores the music data sent to the DSP  210 . When new music data is received without an input of the record key  234 , the temporary storage area deletes the previously stored music data in order to temporarily store the new music data. As explained in the first embodiment, “definitely store and maintain” means that the music data temporarily stored in the temporary storage area of the music data storing section  170  is transferred to the definite storage area so that the storage of the music data can be definitely maintained. Of course, the user can selectively delete any music data stored in the definite storage area using the key input section  230 .  
         [0066]     The definite storage area of the music data storing section  170  is capable of storing music data amounting to about six songs. If the record key  234  is pressed to store new music data while the music data storing section  170  is full, the microprocessor  240  outputs a message informing the full storage state to the display section  180 , for example, “No more music can be stored. Will previously stored music be deleted?”, and waits for a key input from the key input section  230 . If there is a key input to delete, the microprocessor  240  outputs a list of music data stored in the definite storage area of the music data storing section  170  to the display section  180  so that the user can select music to be deleted by placing an indication bar on the music data in the list. If the user presses a delete key, the music data selected by the indication bar will be deleted from the definite storage area. Also, the new music data stored in the temporary storage area will be transferred to the definite storage area to be definitely stored and maintained.  
         [0067]     If the user does not press the record key  234  at step S 714 , the microprocessor  240  will return to step S 704  to output the broadcasting signals to the sound output section  130  and control the DSP  210  to store music data, of which the beginning and end points are recognized and extracted by the music extracting section  500 , in the temporary storage area of the music data storing section  170 .  
         [0068]     According to the second embodiment of the present invention, the digital recorder  200  comprises the music extracting section  500  utilizing a frequency analysis. The digital recorder  200  separates music signals from received broadcasting signals and recognizes the beginning and end of the music, which is being outputted, by a frequency analysis to store the music data. Accordingly, even in case when a user starts to record music after some lapse of time, the music can be recorded and reproduced from the beginning point thereof.  
         [0069]      FIG. 8  is a block diagram showing the inner configuration of a music extracting section  800  utilizing an HMM (hidden Markov model) according to the third embodiment of the present invention.  
         [0070]     In the third embodiment, the music extracting section  800  receives a mixed signal of a plurality of sound sources included in broadcasting signals as an input and retrieves signals of the independent sound sources. The music extracting section  800  collects data for extracting general human speech characteristics and utilizes a hidden Markov model (HMM) trained for such data to extract and remove speech signals. In other words, a hidden Markov model is used to obtain hidden speech information from mixed sound information. The hidden speech information is a Markov process. Under Markov assumption, “any state of a model is dependent only on the state that directly preceded it.” The Markov process refers to a process where transition between states is dependent only on the previous “n” states. The model is termed a n-dimensional model. “n” refers to the number of states that influence the next state.  
         [0071]     An HMM consists of a transition probability for modeling a change of voice with time and an output probability for modeling a spectrum change. The HMM evaluates the similarity between models based on a stochastic estimate of the similarity with a given model, rather than the similarity of an input pattern with a reference pattern. The Viterbi algorithm is utilized to find the most likely sequence of hidden states that preprocess inputted speech data and generate an output similar to the corresponding input.  
         [0072]     Estimation of probabilities is a complicated work because hidden states should be considered. In order to find the best state sequence that most properly explains data, it is required to set a standard for determining the “best”. The estimation of probabilities is associated with training and can be solved by the forward algorithm and the backward algorithm. Generally, the best state sequence is determined using the Viterbi algorithm, which is a dynamic programming method. Also, the Baum-Welch algorithm is applied to estimate parameters of an HMM.  
         [0073]     The music extracting section  800  according to the third embodiment of the present invention extracts acoustic signals and their features utilizing the Baum-Welch algorithm for the estimation of parameters of an HMM. Also, the music extracting section  800  extracts only music signals utilizing the Viterbi algorithm.  
         [0074]     As shown in  FIG. 8 , the music extracting section  800  comprises a sound input section  810 , an MLP (multi-layer perceptron)  820 , a feature extractor  830  and an HMM classifier  840 .  
         [0075]     The sound input section  810  inputs an audio signal including a plurality of acoustic signals, among broadcasting signals received from the DSP  210 , and extracts the acoustic features of the audio signal, for example, zero-crossing information, energy, pitch, spectral frequency and cepstral coefficient. The sound input section  810  divides the audio signal into frames. Each frame has a length of about 10 ms to 30 ms and a different feature value. The frames are laid out in time sequence. The features extracted from the frames are denoted by “Xn”.  
         [0076]     The MLP  820  adopts the algorithm used in the neural network speech recognition as explained in the first embodiment. The MLP  820  obtains a posterior probability showing the possibility (probability P) as to which phoneme “Xn” received from the sound input section  810  belongs to. If an inputted audio signal falls into a speech segment, there is a high probability that the signal is a particular phoneme. Phonemes are outputted to the output terminal of the MLP  820  in the number of k based on P(q 1 |Xn) per Xn, wherein q 1 ˜qk represents the number of phonemes and Xn represents an acoustic feature obtained by the frame analysis at the sound input section  810 .  
         [0077]     The feature extractor  830  implements an operation based on the posterior probability received from the MLP  820  to obtain an entropy Hn which shows a probability distribution within a frame and a dynamism Dn which is a probability of a variation between frames. The feature extractor  830  outputs the entropy and dynamism features to the HMM classifier  840 . If an audio signal is speech, the entropy will be near zero, while the dynamism will be high because of the large variation between frames. On the contrary, if the signal is music, it will have a high entropy because of the wide probability distribution and a low dynamism because of the less variation with time.  
         [0078]     Following equations 1 and 2 are for obtaining entropy Hn and dynamism Dn, respectively.  
               H   n     =       -     1   N       ⁢       Q     m   =     n   -     N   2             n   +     N   2         ⁢       Q   K       k   =   1       ⁢     P   ⁡     (       q   k     ❘     χ   m       )       ⁢     log   2     ⁢     P   ⁡     (       q   k     ❘     χ   m       )                 [     Equation   ⁢           ⁢   1     ]                 D   n     =       -     1   N       ⁢       Q     m   =     n   -     N   2             n   +     N   2         ⁢           Q   K       k   =   1       ⁡     [       P   ⁡     (       q   k     ❘     χ   m       )       -     P   ⁡     (       q   k     ❘     χ     m   +   1         )         ]       2               [     Equation   ⁢           ⁢   2     ]             
 
         [0079]     The HMM classifier  840  classifies audio signals into a speech class and a music class based on the entropy Hn and dynamism Dn received from the feature extractor  830 , utilizing the Baum-Welch algorithm and the Viterbi algorithm. The states in each class are all the same but present in a plural number. The HMM classifier  840  learns an HMM to optimize the probability of transition between states based on the two feature parameters (Hn, Dn) utilizing the Baum-Welch algorithm. The initial value before learning is set to a predetermined value. Actually, the HMM classifier  840  forms a table based on the received feature parameters and the learned HMM, when classifying audio signals into a speech class and a music class. Also, the HMM classifier  840  calculates the class to which an inputted audio signal belongs, using the Viterbi algorithm, and finally determines whether the signal belongs to a speech class or a music class.  
         [0080]     The Baum-Welch algorithm and the Viterbi algorithm, both of which are utilized by the HMM classifier  840 , will be explained in more detail.  
         [0081]     After selecting a suitable model that best matches an observation sequence, it is required to determine the best state sequence of the model that generates the observation sequence. Generally, the Viterbi algorithm, which is a dynamic programming algorithm, is used to determine the best state of a model.  
         [0082]     1. The Viterbi Algorithm  
         [0083]     Given an observation sequence o and a model λ, the Viterbi algorithm is the most efficient method to determine a state sequence Q which generates the observation sequence o with the maximum probability. The probability of generating an observation sequence based on the observation sequence o and the model λ is P(q 1 , q 2 , . . . qT|o, λ).  
         [0084]      FIG. 9  shows the principle of the Viterbi algorithm for finding the most likely state sequence with the maximum probability.  
         [0085]     In other words,  FIG. 9  shows steps for determining the sequence of states that transit with the highest probability, among the state transitions from time t to time t+1. The Viterbi algorithm computes the state path with the maximum probability through the following steps:  
                                                   {circumflex over (1)} Initialization:   δ 1 (i) = σ i b i (o 1 ), 1DiDN, ψ 1 (i) = 0               {circumflex over (2)} Recursion:               δ   1     ⁡     (   j   )       =             max             1   ⁢   DiDn           ⁢           [         δ     t   -   1       ⁡     (   i   )       ⁢     a   ij       ]     ⁢       b   j     ⁡     (     o   t     )       ⁢           ⁢   2   ⁢   DtDT                                           ψ   1     ⁡     (   j   )       =             arg   ⁢           ⁢   max               1   ⁢   DiDn           ⁡     [         δ     t   -   1       ⁡     (   i   )       ⁢     a   ij       ]         ,     1   ⁢   DjDN                         {circumflex over (3)} Termination:               P   *     =           max             1   ⁢   DiDN           ⁡     [       δ   T     ⁡     (   i   )       ]         ,       a   T   *     =             arg   ⁢           ⁢   max               1   ⁢   DiDN           ⁡     [       δ   T     ⁡     (   i   )       ]                             {circumflex over (4)} State Sequence Backtracking:               q   t   *     =       ψ     t   +   1       ⁡     (     q     t   +   1     *     )         ,     t   =     T   -   1       ,     T   -   2     ,   …   ⁢           ,   1                          
 
         [0086]     In the above algorithm, ψ t (i) is a variable for maintaining the optimal path for transition to state i at time t. ψ t (i) calculates the state path with the maximum probability by the equation  
             ψ   t     ⁡     (   i   )       =             arg   ⁢           ⁢   max               1   ⁢   DiDN           ⁡     [         δ     t   -   1       ⁡     (   i   )       ⁢     a   ij       ]         ,       
 
 using the most likely path δ t−1  to the previous state (t−1) and the transition matrix to state j at time t. 
 
         [0087]     In  FIG. 9 , δ t (j) shows the probability of the most likely path among paths ending in state j and can be denoted by equation 3.  
                 δ   t     ⁡     (   i   )       =       max       q   1     ,     q   2     ,   …   ⁢           ,     q     t   -   1           ⁢           ⁢     P   ⁡     (       q   1     ,     q   2     ,   …   ⁢           ,     q   t     ,     =   i     ,     o   1     ,     o   2     ,   …   ⁢           ,       o   t     ❘   λ       )                 [     Equation   ⁢           ⁢   3     ]             
 
         [0088]     Equation 4 can be derived from equation 3 by induction.  
                 δ     t   +   1       ⁡     (   j   )       =       max   i     ⁢           ⁢       [       δ   t     ⁢     a   ij       ]     ⁢     Eb   ⁡     (     o     t   +   1       )                   [     Equation   ⁢           ⁢   4     ]             
 
         [0089]     Equation 4 enables to obtain the state sequence with the maximum probability at time t+1, as well as at time t.  
         [0090]     2. The Baum-Welch Algorithm  
         [0091]     It is required to first select a model that best matches an observation sequence and set the optimal sequence of states within the model. It is then required to determine parameters of the model λ=(π, A, B), which maximize P(o|λ) with respect to the observation sequence o. Because of the complexity of models, it is difficult to determine the model parameters by an analytic method. Therefore, the Baum-Welch algorithm is used for parameter reestimation (training).  
         [0092]     The Baum-Welch algorithm forms an initial model λ 0  and a new model λ based on the initial model and the observation sequence o. The Baum-Welch algorithm generates a new model by modifying the model parameters until the difference between the probability of a new model and that of the previous model is over a “predetermined value”.  
         [0093]     The Baum-Welch algorithm additionally defines two new parameters according to equations 5 and 6.  
               ξ   ⁡     (     i   ,   j     )       =           α   1     ⁡     (   i   )       ⁢     a   ij     ⁢       b   j     ⁡     (     o     t   +   1       )       ⁢       β     t   +   1       ⁡     (   j   )           P   ⁡     (     o   ❘   λ     )                 [     Equation   ⁢           ⁢   5     ]             
 
         [0094]     Equation 5 shows the probability of being in state i at time t and state j at time t+1. In this equation, α is a forward parameter of the forward algorithm, and β is a backward parameter of the backward algorithm. If  
           Q     T   -   1         t   =   1       ⁢     ξ   ⁡     (     i   ,   j     )           
 
 is applied to equation 5, an expected value of the number of transitions from state i to state j at the observation sequence o can be obtained.  
                 γ   t     ⁡     (   i   )       =         Q   N       j   =   1       ⁢       ξ   t     ⁡     (     i   ,   j     )                 [     Equation   ⁢           ⁢   6     ]             
 
         [0095]     Equation 6 shows the probability of being in state i with the given observation sequence at time t. If  
           Q   T       t   =   1       ⁢       γ   t     ⁡     (   i   )           
 
 is applied to equation 6, it is possible to obtain an expected value of the number of emissions at state i at the observation sequence o. 
 
         [0096]     Through the methods mentioned above, the HMM classifier  840  selects music signals among inputted audio signals and outputs the selected signals to the DSP  210 .  
         [0097]     Hereinafter, the operation of the digital recorder, which outputs only music signals using the music extracting section  800 , will be explained in more detail with reference to  FIG. 10 .  
         [0098]      FIG. 10  is a flow chart showing a process of selectively storing music utilizing an HMM according to the third embodiment of the present invention.  
         [0099]     When a broadcasting signal received by the antenna  110  is sent to the tuner  120 , the tuner  120  outputs the signal to the sound output section  130 . At the same time, the tuner  120  outputs the signal to the music extracting section  800  via the DSP  210  (S 1020 ). The broadcasting signal inputted to the music extracting section  800  is sent to the sound input section  810 . The sound input section  810  divides an audio signal into frames and extracts the acoustic features of the audio signal, for example, zero-crossing information, energy, pitch, spectral frequency and cepstral coefficient. The sound input section  810  sends the extracted acoustic features to the MLP  820  (S 1040 ).  
         [0100]     The MLP  820  obtains a posterior probability showing the possibility (probability P) as to the phoneme to which the acoustic features received from the sound input section  810  belong, and outputs the posterior probability to the feature extractor  830  (S 1060 ). The feature extractor  830  obtains the entropy Hn and dynamism Dn features based on the posterior probability received from the MLP  820  (S 1080 ). The feature extractor  830  outputs the obtained entropy Hn and dynamism Dn to the HMM classifier  840 . The HMM classifier  840  selects only music data based on the entropy Hn and dynamism Dn received from the feature extractor  830 , utilizing the Baum-Welch algorithm and the Viterbi algorithm. The HMM classifier  840  outputs the selected music data to the DSP  210  (S 1100 ).  
         [0101]     The DSP  210  encodes the music data received from the HMM classifier  840  into an MP3 music file, using the encoder  214 , and temporarily stores the encoded data in the temporary storage area of the music data storing section  170  (S 1120 ). At the same time, the DSP  210  outputs the broadcasting signals, including the music signal which is being temporarily stored, to the sound output section  130 . When music, to which the user is listening, is temporarily stored in the temporary storage area of the music data storing section  170 , the beginning and end of the music are recognized by the process as explained in the second embodiment. In this regard, the microprocessor  240 , instead of the music extracting section  220 ,  500 ,  800 , can be configured to have a function to recognize the beginning of a music signal.  
         [0102]     If the record key  234  provided in the key input section  230  is pressed while broadcasting signals including a music signal are being outputted to the sound output section  130 , the microprocessor  240  will control the DSP  210  to recognize the beginning and end points of the music data temporarily stored in the temporary storage area based on the beginning/end data stored in the non-music storage area of the music data storing section  170 . The microprocessor  240  will then transfer the music data to the definite storage area in order to definitely store the music data (S 1160 ). The meaning of “definitely store and maintain” is as explained in the second embodiment.  
         [0103]     If the user does not press the record key  234 , the microprocessor  240  will return to step S 1020  and will repeat the process of outputting the broadcasting signals to the sound output section  130  and storing only music signals among the currently outputted broadcasting signals. The user can select and reproduce desired music from the music data stored in the music data storing section  170 .  
         [0104]     According to the third embodiment of the present invention, the digital recorder  200 , includes the music extracting section  500  utilizing the HMM in order to classify broadcasting signals into speech signals and music signals and store the music signals only.  
         [0105]     Although preferred embodiments of the present invention have been described for illustrative purposes, those skilled in the art will appreciate that various modifications, additions and substitutions are possible, without departing from the scope and spirit of the invention as disclosed in the accompanying claims.  
         [0106]     It is possible to form a music extracting section utilizing an ICA (independent component analysis) based on speech recognition technology. Generally, “speech recognition” is a technique for recognizing or identifying human voice by a mechanical (computer) analysis. Human speech sounds have peculiar frequencies depending on the shape of mouth and the position of tongue which change according to the pronunciation. Human speech signals can be recognized by converting pronounced speech to an electrical signal and extracting a variety of features of a speech signal. Therefore, it is possible to extract and remove speech signals from broadcasting signals using a music extracting section based on the speech recognition technology, thereby outputting music signals only.  
         [0107]     In the preferred embodiments of the present invention, the music data storing section  170  temporarily stores music data. Only when the record key  234  is pressed, the music data storing section  170  definitely stores and maintains the music data. However, it is also possible to provide a temporary memory to temporarily store one or more music data extracted by the music extracting section  220 . Music data being outputted to the sound output section  130  and extracted by the music extracting section  220  can be stored in the temporary memory. When the record key  234  is pressed, the music data stored in the temporary memory can be transferred to the music data storing section  170  to be definitely stored. When the record key  234  is not pressed, the music data stored in the temporary memory can be deleted so that new music data can be stored in the temporary memory.  
         [0108]     As described above, the present invention provides a digital recorder and a method for not only outputting received broadcasting signals as an audible sound, but also selectively storing music signals included in the broadcasting signals as digital music data, utilizing an artificial neural network, a frequency analysis or a hidden Markov model.  
         [0109]     The digital recorder separates music from the received broadcasting signals and recognizes the beginning and end of the music to completely store the music from beginning to end. Accordingly, it is possible to record and reproduce music from the beginning thereof, even in case when a user starts to record the music after some lapse of time.  
         [0110]     The present invention can solve inconvenience and trouble to press the record key twice to record music when begins and finish the recording operation when the music ends. Also, the present invention eliminates the need to pay close attention to correctly recognize the beginning and end of a musical selection.