Patent Publication Number: US-9886962-B2

Title: Extracting audio fingerprints in the compressed domain

Description:
BACKGROUND 
     In many applications it is useful to be able to identify or verify an audio file by analyzing the content of its audio signal. A portion of the audio file may be acquired to form an audio fingerprint of the file for the purpose of identifying or verifying the file. However, creating a suitable audio fingerprint of the audio file in a format required for processing may be a time-consuming task. 
     BRIEF SUMMARY 
     According to an embodiment of the disclosed subject matter, a computer-implemented method performed by a data processing apparatus includes receiving an audio signal that includes a frequency-domain representation of an audio file, extracting, from the audio signal, a plurality of frequency-domain data values that correspond to at least a portion of the audio file, compressing the plurality of data values to form a compressed frequency domain value file, and transmitting the compressed frequency domain value file to a server to identify the audio file. 
     According to an embodiment of the disclosed subject matter, a decoder includes an input module that receives an audio signal including a frequency-domain representation of an audio file, a bitstream unpacking module that unpacks a bitstream of the audio signal to separate a side information portion of the audio signal from a main data portion of the audio signal, a Huffman decoding module that decodes the main portion of the data, an inverse quantization module that executes inverse quantization on the decoded data to form a frequency-domain representation of the audio file, a binning and compression module to bin the frequency-domain representation of the audio file and compress the binned data to form a compressed frequency domain value file, and an output module to transmit the compressed frequency domain value file to a server to identify the audio file. 
     According to an embodiment of the disclosed subject matter, means for receiving an audio signal that includes a frequency-domain representation of an audio file, extracting, from the audio signal, a plurality of frequency-domain data values that correspond to at least a portion of the audio file, compressing the plurality of data values to form a compressed frequency domain value file, and transmitting the compressed frequency domain value file to a server to identify the audio file are provided. 
     Additional features, advantages, and embodiments of the disclosed subject matter may be set forth or apparent from consideration of the following detailed description, drawings, and claims. Moreover, it is to be understood that both the foregoing summary and the following detailed description are illustrative and are intended to provide further explanation without limiting the scope of the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings, which are included to provide a further understanding of the disclosed subject matter, are incorporated in and constitute a part of this specification. The drawings also illustrate embodiments of the disclosed subject matter and together with the detailed description serve to explain the principles of embodiments of the disclosed subject matter. No attempt is made to show structural details in more detail than may be necessary for a fundamental understanding of the disclosed subject matter and various ways in which it may be practiced. 
         FIG. 1  shows a conventional MP3 decoder. 
         FIG. 2  shows a conventional audio fingerprint process. 
         FIG. 3  shows a decoder and audio fingerprint process according to an embodiment of the disclosed subject matter. 
         FIG. 4  shows a flowchart of an audio fingerprint process according to an embodiment of the disclosed subject matter. 
         FIG. 5  shows a computing device according to an embodiment of the disclosed subject matter. 
         FIG. 6  shows an example network and system configuration according to an embodiment of the disclosed subject matter 
     
    
    
     DETAILED DESCRIPTION 
     Various aspects or features of this disclosure are described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In this specification, numerous details are set forth in order to provide a thorough understanding of this disclosure. It should be understood, however, that certain aspects of disclosure may be practiced without these specific details, or with other methods, components, materials, etc. In other instances, well-known structures and devices are shown in block diagram form to facilitate describing the subject disclosure. 
     Embodiments of the subject matter disclosed herein relate generally to a method and system of creating audio fingerprints to identify digital audio files. In embodiments disclosed herein, compressed frequency domain values may be generated for an audio file by using a shortened decoding process that does not require completely decoding a file. A fingerprint process is further disclosed which utilizes the compressed frequency domain values and does not require an uncompressed file input or audio spectral image computation. A mobile device or a device with limited computing power, for example, may employ the techniques described herein to generate audio fingerprints with minimized computation, improved speed and improved accuracy. 
     A digital audio file may be encoded in any of various formats, including compressed and uncompressed formats. Compressed digital audio file formats include, for example, MPEG-1 or MPEG-2 Audio Layer III (MP3), Advanced Audio Coding (AAC), or Windows Media Audio (WMA). Uncompressed audio file formats include, for example, waveform (WAV), which is a popular format for CD&#39;s and encoded using pulse-code modulation (PCM). An audio file in an uncompressed format may provide superior sound quality compared to a compressed format, however, compressed formats generally result in a file size far smaller than uncompressed formats. Accordingly, compressed formats are preferred for audio files that are uploaded/downloaded between a user device and a server, for example, in order to decrease traffic and to lower file transfer time. 
     Many conventional audio fingerprinting techniques are executed on uncompressed audio files, usually in PCM format. To use such audio fingerprinting techniques, an audio file stored in a compressed format, such as MP3, must first be fully decoded to PCM so that the audio file may be processed to compute the audio fingerprint. According to the present embodiments disclosed herein, a full decoding operation may be avoided by extracting intermediate frequency-domain data during a modified decoding process. The extracted frequency-domain data may then be used to create an audio fingerprint. 
     Embodiments disclosed herein generally are not limited to relying upon extracting frequency-domain data in a single form. Suitable frequency-domain data for an audio file may be available for extraction in a number of forms depending at least in part on the way that the audio file was encoded. 
     Many different encoding processes exist for encoding a digital audio file. Such encoding processes may include operations comprising sampling the audio file in the time-domain and transforming the samples to the frequency-domain for various processing purposes. For example, there are many different ways to encode an audio file in the MP3 format, but MP3 encoding generally includes passing the audio signal of the file through a polyphase filterbank for dividing the signal into subbands and performing time-domain to frequency-domain mapping. While in the frequency-domain, a psychoacoustic model is applied to discard audio data that is likely to be inaudible to human ears. The remaining data is quantized and formatted to be assembled into frames. A modified discrete cosine transform (MDCT) is used at the output of the polyphase filterbank and at the output of the psychoacoustics in order to limit the sources of output distortion. 
     Applying the disclosed subject matter to an MP3 format audio file, the frequency-domain data representation of audio data to be extracted may comprise the MDCT coefficients. This data may be extracted in a modified, partial decode of the MP3 file. While the present embodiments are not limited in application to the MP3 format, a description of an embodiment for extracting the frequency-domain data representation of audio data in the form of MDCT coefficients from an MP3 audio file will be instructive. First, a conventional MP3 decoding process will be briefly described. 
       FIG. 1  illustrates an overview of an MP3 decoder  100  that receives an MP3 audio signal as input and outputs a PCM audio signal. Bitstream unpacking module  110  unpacks the MP3 audio data, separating the side information from the main audio data. Subsequently, Huffman decoding module  120  decodes main audio data received from the bitstream unpacking module. An inverse quantization module  130  restores the resolution of the decoded main data. The inverse quantized data is passed through a synthesis filterbank  140 , which applies an inverse MDCT to the data. The output of the filterbank is PCM format data. 
     Conventional audio fingerprint techniques often use the PCM format data to create audio fingerprints. An overview of an example conventional audio fingerprint process  200  ( FIG. 2 ) that may be used to compute audio fingerprints from PCM format data will also be instructive. While a brief summary is provided below, the process  200  is described in greater detail in “Content Fingerprinting Using Wavelets,” S. Baluja and M. Covell, In Proceedings of the Conference of Visual Media Production, IET (2006) (hereinafter, “Baluja”). 
     Referring to  FIG. 2 , processing the PCM sample to create an audio fingerprint computation begins with calculation of a short time power spectrum at operation  210 , a potentially time consuming operation that may be performed by using Fast Fourier transforms (FFT). The power spectrum is then filtered into a small number of perceptually motivated frequency bands, then grouped to form “auditory image” samples at operation  220 . Wavelets are computed on each auditory image at  230 , and a binary representation of the top t % wavelets is created, e.g., based on the sign of each wavelet. At operation  230  a Min-Hash technique is used to create sub-fingerprints by reducing the size of signatures from the intermediate wavelet representation to a compact representation of p values. In other words, after operations  210  through  230  are complete, each spectral image (or equivalent-length audio segment) is represented by a series of p 8-bit integers, i.e., the sub-fingerprint. A Locality-Sensitive Hashing (LSH) technique is used at operation  240  to select which sub-fingerprints will be used for matching purposes in searching for the audio file in a database, that is, which sub-fingerprints comprise the audio fingerprint used to find a matching audio file. 
     The audio fingerprint process  200  shown in  FIG. 2  describes an overview of one example process for deriving an audio fingerprint from PCM format data. A person of ordinary skill in the art will be aware that other audio fingerprint processes exist, and more specifically, other audio fingerprint processes exist with a common starting point of processing auditory image data. 
     Audio fingerprint processes disclosed herein may differ from the process  200  in that the requirements of fully decoding to PCM format and of creating spectral auditory images are eliminated. Instead, representative frequency information may be extracted directly from the encoded audio signal and a frequency representation may be subsequently processed in accordance with the remaining techniques. Therefore, in an illustrative use case of an MP3 audio file, the audio fingerprint process of the present invention may receive an input based on the MDCT coefficients and process the input to create audio fingerprints, e.g, using operations  230  to  240  in  FIG. 2 . In this manner the overall audio fingerprint process may be shortened. 
       FIG. 3  illustrates an embodiment of a decoder  300  and fingerprint process  360  according to the presently disclosed invention. A bitstream unpacking module  310  unpacks the MP3 audio data, separating the side information from the main audio data. A Huffman decoding module  320  decodes main audio data received from the bitstream unpacking module. An inverse quantization module  330  resolves the resolution of the decoded main data. The inverse quantized data may comprise a floating point representation of the audio data in the frequency domain. The coefficients of the frequency representation may be sent to a binning and compression module  340 . The coefficients may be binned and concatenated for a sequence of frames to generate a frequency representation file of the audio file, herein referred to as a compressed frequency domain value file. A more compact frequency representation file can be obtained by quantizing each coefficient in a frame with a variable step that depends on the maximum (and optionally minimum) value in that frame. 
     Further compression of the frequency representation file can be obtained by exploiting temporal redundancy and predicting the quantized coefficients in a frame by using the previous coefficients. For example, in a case of the binning and compression module binning the coefficients into 32 bins, each represented by a 32-bit floating point value, a 256 level quantization provides 3.2:1 to 3.5:1 compression. Temporal prediction may be capable of doubling that amount. 
       FIG. 3  also shows an audio fingerprint process  360  that receives the output of the decoder  300  and computes an audio fingerprint. At operation  360 , a wavelet transform is applied to the compressed frequency domain value. At operation  370  a Min-Hash technique is used to create compressed frequency domain values by reducing the size of signatures from the intermediate wavelet representation to a compact representation of p values. A Locality-Sensitive Hashing (LSH) technique is used at operation  380  to select which compressed frequency domain values will be used for matching purposes in searching for the audio file in a database, that is, which compressed frequency domain values comprise the audio fingerprint used to find a matching audio file. 
       FIG. 4  illustrates a flowchart of an embodiment of the present invention. At operation  410 , an audio signal that includes a frequency-domain representation of an audio file is received. At operation  420 , a plurality of frequency-domain data values that correspond to at least a portion of the audio file are extracted from the audio signal. At operation  430  the plurality of data values are compressed to form a compressed frequency domain value file. At operation  440  the compressed frequency domain value file is transmitted to a server to identify the audio file. 
     It should be understood that embodiments of the audio fingerprint technique of the subject invention is applicable in many contexts, and that the inherent benefits of using the techniques of the subject invention may be exploited in various ways. An illustrative example will now be provided in which embodiments of the present invention are applied within the context of an “audio locker” network service. 
     In an audio locker network service, a user may maintain an account with the service, having one or more audio files associated with the account. By logging into the account remotely, the user may have access to his/her full library of audio files anywhere that network service is available. When the user is initially uploading audio files (hereinafter, also referred to as “tracks”) to the service, it may be useful for the audio locker to first check whether an identical track is already present in an associated service, such as an online store. If an identical track exists in the store and has appropriate rights, the audio locker may provide the option of foregoing the track upload and, for that user, stream the canonical store copy of the track instead. This functionality will be referred to herein as “scan and match.” 
     During a scan and match process, the track that the user intends to upload must be identified, typically by an audio fingerprint. In order to avoid a full upload to the network service but still reliably verify ownership of the track with a minimum processing load on the client, a scan and match process may work in four phases. First, the metadata and a hash of the audio track is uploaded to the network service. Second, the service verifies the presence in the store of one or more tracks that have the required licensing rights. These licensed tracks are called matching candidates. When one or more matching candidates exist, the service may request a proof of ownership from the client, e.g., a short snippet selected from the user audio at a random position. Third, the client extracts the audio snippet as requested by the server and uploads it. Depending on the audio format, transcoding may be necessary. Fourth, the snippet is used to match the audio against the matching candidates. 
     The audio snippet is uploaded to the server and compared to store audio by using audio fingerprints. In a conventional audio locker, to constrain bandwidth while maintaining a reasonable precision and recall at least fifteen seconds of audio typically need to be uploaded, in an MP3 format of at least 128 Kbps. If the user audio is not already in the required format, then transcoding is necessary. Transcoding involves decoding of the audio file in PCM and re-encoding in the required output format. The encoding is typically time consuming and requires approximately one order of magnitude more resources than decoding. 
     As described above regarding  FIG. 2 , the audio fingerprints are typically computed on PCM samples (raw audio samples), so the snippet needs to be decompressed again by the server. After decoding, conventional fingerprint computation requires calculation of short time power spectrum, another time consuming operation. 
     In contrast, applying the embodiments disclosed herein, frequency-domain information may be extracted directly from the encoded stream to avoid transcoding on the client side, as well as avoid full decoding and spectral calculations on the server side. The compressed frequency domain value file may be generated on client side, i.e., the client may implement the decoder  300  of  FIG. 3 . The server may be configured to implement the audio fingerprint process  390  of  FIG. 3 , or some other audio fingerprint process that can receive the compressed frequency domain value file as an input. 
     This technique may present several advantages over existing solutions. For example, the compressed frequency domain value file can be generated with minimal computation on the client. Another advantage is, if the user audio is already in one of the supported compressed formats (MP3, for example), no transcoding is necessary. Experiments showed that as long as the sample rate is known, encoding bitrate does not impact precision and recall. 
     In addition, a quantized representation of the binned frequency coefficients can be very compact and a compressed frequency domain value file of a long audio segment, or even of the entire audio file, can be uploaded instead of just a 15 second snippet. Empirically, the frequency representation extracted from the coefficients in the compressed domain has been found to be more precise than FFT alternatives, and it allows a better discrimination between different versions of the same audio file (explicit vs. edited versions, remasterings, etc.). Furthermore, binned coefficients can be collected during the first step of scan and match and transmitted to the server together with metadata and audio hash. This can greatly improve latency as the server can match both metadata and audio, verify ownership, and make a decision without a second exchange with the client 
     In situations in which the systems discussed here collect personal information about users, or may make use of personal information, the users may be provided with an opportunity to control whether programs or features collect user information (e.g., information about a user&#39;s social network, social actions or activities, profession, a user&#39;s preferences, or a user&#39;s current location), or to control whether and/or how to receive content from the content server that may be more relevant to the user. In addition, certain data may be treated in one or more ways before it is stored or used, so that personally identifiable information is removed. For example, a user&#39;s identity may be treated so that no personally identifiable information can be determined for the user, or a user&#39;s geographic location may be generalized where location information is obtained (such as to a city, ZIP code, or state level), so that a particular location of a user cannot be determined. Thus, the user may have control over how information is collected about the user and used by a system as disclosed herein. 
     Embodiments of the presently disclosed subject matter may be implemented in and used with a variety of component and network architectures.  FIG. 5  is an example computing device  20  suitable for implementing embodiments of the presently disclosed subject matter. The device  20  may be a client device implemented as, for example, a desktop or laptop computer, or a mobile computing device such as a smart phone, tablet, or the like. The device  20  may include a bus  21  which interconnects major components of the computer  20 , such as a central processor  24 , a memory  27  such as Random Access Memory (RAM), Read Only Memory (ROM), flash RAM, or the like, a user display  22  such as a display screen, a user input interface  26 , which may include one or more controllers and associated user input devices such as a keyboard, mouse, touch screen, and the like, a fixed storage  23  such as a hard drive, flash storage, and the like, a removable media component  25  operative to control and receive an optical disk, flash drive, and the like, and a network interface  29  operable to communicate with one or more remote devices via a suitable network connection. 
     The bus  21  allows data communication between the central processor  24  and one or more memory components, which may include RAM, ROM, and other memory, as previously noted. Typically RAM is the main memory into which an operating system and application programs are loaded. A ROM or flash memory component can contain, among other code, the Basic Input-Output system (BIOS) which controls basic hardware operation such as the interaction with peripheral components. Applications resident with the computer  20  are generally stored on and accessed via a computer readable medium, such as a hard disk drive (e.g., fixed storage  23 ), an optical drive, floppy disk, or other storage medium. 
     The fixed storage  23  may be integral with the computer  20  or may be separate and accessed through other interfaces. The network interface  29  may provide a direct connection to a remote server via a wired or wireless connection. The network interface  29  may provide such connection using any suitable technique and protocol as will be readily understood by one of skill in the art, including digital cellular telephone, WiFi, Bluetooth®, near-field, and the like. For example, the network interface  29  may allow the computer to communicate with other computers via one or more local, wide-area, or other communication networks, as described in further detail below. 
     Many other devices or components (not shown) may be connected in a similar manner (e.g., document scanners, digital cameras and so on). Conversely, all of the components shown in  FIG. 5  need not be present to practice the present disclosure. The components can be interconnected in different ways from that shown. The operation of a computer such as that shown in  FIG. 5  is readily known in the art and is not discussed in detail in this application. Code to implement the present disclosure can be stored in computer-readable storage media such as one or more of the memory  27 , fixed storage  23 , removable media  25 , or on a remote storage location. 
       FIG. 6  shows an example arrangement according to an embodiment of the disclosed subject matter. One or more devices or systems  10 ,  11 , such as remote services or service providers  11 , user devices  10  such as local computers, smart phones, tablet computing devices, and the like, may connect to other devices via one or more networks  7 . The network may be a local network, wide-area network, the Internet, or any other suitable communication network or networks, and may be implemented on any suitable platform including wired and/or wireless networks. The devices  10 ,  11  may communicate with one or more remote computer systems, such as processing units  14 , databases  15 , and user interface systems  13 . In some cases, the devices  10 ,  11  may communicate with a user-facing interface system  13 , which may provide access to one or more other systems such as a database  15 , a processing unit  14 , or the like. For example, the user interface  13  may be a user-accessible web page that provides data from one or more other computer systems. The user interface  13  may provide different interfaces to different clients, such as where a human-readable web page is provided to a web browser client on a user device  10 , and a computer-readable API or other interface is provided to a remote service client  11 . 
     The user interface  13 , database  15 , and/or processing units  14  may be part of an integral system, or may include multiple computer systems communicating via a private network, the Internet, or any other suitable network. One or more processing units  14  may be, for example, part of a distributed system such as a cloud-based computing system, audio locker, online store, search engine, content delivery system, or the like, which may also include or communicate with a database  15  and/or user interface  13 . In some arrangements, an analysis system  5  may provide back-end processing, such as where stored or acquired data is pre-processed by the analysis system  5  before delivery to the processing unit  14 , database  15 , and/or user interface  13 . For example, a machine learning system  5  may provide various prediction models, data analysis, or the like to one or more other systems  13 ,  14 ,  15 . 
     More generally, various embodiments of the presently disclosed subject matter may include or be embodied in the form of computer-implemented processes and apparatuses for practicing those processes. Embodiments also may be embodied in the form of a computer program product having computer program code containing instructions embodied in non-transitory and/or tangible media, such as floppy diskettes, CD-ROMs, hard drives, USB (universal serial bus) drives, or any other machine readable storage medium, such that when the computer program code is loaded into and executed by a computer, the computer becomes an apparatus for practicing embodiments of the disclosed subject matter. Embodiments also may be embodied in the form of computer program code, for example, whether stored in a storage medium, loaded into and/or executed by a computer, or transmitted over some transmission medium, such as over electrical wiring or cabling, through fiber optics, or via electromagnetic radiation, such that when the computer program code is loaded into and executed by a computer, the computer becomes an apparatus for practicing embodiments of the disclosed subject matter. When implemented on a general-purpose microprocessor, the computer program code segments configure the microprocessor to create specific logic circuits. 
     In some configurations, a set of computer-readable instructions stored on a computer-readable storage medium may be implemented by a general-purpose processor, which may transform the general-purpose processor or a device containing the general-purpose processor into a special-purpose device configured to implement or carry out the instructions. Embodiments may be implemented using hardware that may include a processor, such as a general purpose microprocessor and/or an Application Specific Integrated Circuit (ASIC) that embodies all or part of the techniques according to embodiments of the disclosed subject matter in hardware and/or firmware. The processor may be coupled to memory, such as RAM, ROM, flash memory, a hard disk or any other device capable of storing electronic information. The memory may store instructions adapted to be executed by the processor to perform the techniques according to embodiments of the disclosed subject matter. 
     The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit embodiments of the disclosed subject matter to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to explain the principles of embodiments of the disclosed subject matter and their practical applications, to thereby enable others skilled in the art to utilize those embodiments as well as various embodiments with various modifications as may be suited to the particular use contemplated.