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An audio search engine is a web-based search engine which crawls the web for audio content. The information can consist of web pages, images, audio files, or another type of document. Various techniques exist for research on these engines.
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An audio search engine is a web-based search engine which crawls the web for audio content. The information can consist of web pages, images, audio files, or another type of document. Various techniques exist for research on these engines.
Text entered into a search bar by the user is compared to the search engine's database. Matching results are accompanied by a brief description of the audio file and its characteristics such as sample frequency, bit rate, type of file, length, duration, or coding type. The user is given the option of downloading the resulting files.
The Query by Example (QBE) system is a searching algorithm that uses content-based image retrieval (CBIR). Keywords are generated from the analysed image. These keywords are used to search for audio files in the database. The results of the search are displayed according to the user preferences regarding to the type of file (wav, mp3, aiff…) or other characteristics.
In audio search from audio, the user must play the audio of a song either with a music player, by singing or by humming to the computer microphone. Subsequently, a sound pattern, A, is derived from the audio waveform, and a frequency representation is derived from its Fourier Transform. This pattern will be matched with a pattern, B, corresponding to the waveform and transform of sound files found in the database. All those audio files in the database whose patterns are similar to the pattern searched will be displayed as search results.
Audio search has evolved slowly through several basic search formats which exist today and all use keywords. The keywords for each search can be found in the title of the media, any text attached to the media and content linked web pages, also defined by authors and users of video hosted resources.
Some search engines can search recorded speech such as podcasts, though this can be difficult if there is background noise. Around 40 phonemes exist in every language with about 400 in all spoken languages. Rather than applying a text search algorithm after speech-to-text processing is completed, some engines use a phonetic search algorithm to find results within the spoken word. Others work by listening to the entire podcast and creating a text transcription.
Applications as Munax, use several independent ranking algorithms processes, that the inverted index together with hundreds of search parameters to produce the final ranking for each document. Also like Shazam that works by analyzing the captured sound and seeking a match based on an acoustic fingerprint in a database of more than 11 million songs. Shazam identifies songs based on an audio fingerprint based on a time-frequency graph called a spectrogram. Shazam stores a catalogue of audio fingerprints in a database. The user tags a song for 10 seconds and the application creates an audio fingerprint. Once it creates the fingerprint of the audio, Shazam starts the search for matches in the database. If there is a match, it returns the information to the user; otherwise it returns a "song not known" dialogue. Shazam can identify prerecorded music being broadcast from any source, such as a radio, television, cinema or music in a club, provided that the background noise level is not high enough to prevent an acoustic fingerprint being taken, and that the song is present in the software's database.[ citation needed ]
Content-based image retrieval, also known as query by image content and content-based visual information retrieval (CBVIR), is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases. Content-based image retrieval is opposed to traditional concept-based approaches.
Desktop search tools search within a user's own computer files as opposed to searching the Internet. These tools are designed to find information on the user's PC, including web browser history, e-mail archives, text documents, sound files, images, and video. A variety of desktop search programs are now available; see this list for examples. Most desktop search programs are standalone applications. Desktop search products are software alternatives to the search software included in the operating system, helping users sift through desktop files, emails, attachments, and more.
A video search engine is a web-based search engine which crawls the web for video content. Some video search engines parse externally hosted content while others allow content to be uploaded and hosted on their own servers. Some engines also allow users to search by video format type and by length of the clip. The video search results are usually accompanied by a thumbnail view of the video.
A search engine results page (SERP) is a webpage that is displayed by a search engine in response to a query by a user. The main component of a SERP is the listing of results that are returned by the search engine in response to a keyword query.
Google Images is a search engine owned by Google that allows users to search the World Wide Web for images. It was introduced on July 12, 2001, due to a demand for pictures of the green Versace dress of Jennifer Lopez worn in February 2000. In 2011, reverse image search functionality was added.
A tag editor is an app that can add, edit, or remove embedded metadata on multimedia file formats. Content creators, such as musicians, photographers, podcasters, and video producers, may need to properly label and manage their creations, adding such details as title, creator, date of creation, and copyright notice.
Multimedia search enables information search using queries in multiple data types including text and other multimedia formats. Multimedia search can be implemented through multimodal search interfaces, i.e., interfaces that allow to submit search queries not only as textual requests, but also through other media. We can distinguish two methodologies in multimedia search:
Image meta search is a type of search engine specialised on finding pictures, images, animations etc. Like the text search, image search is an information retrieval system designed to help to find information on the Internet and it allows the user to look for images etc. using keywords or search phrases and to receive a set of thumbnail images, sorted by relevancy.
Audio mining is a technique by which the content of an audio signal can be automatically analyzed and searched. It is most commonly used in the field of automatic speech recognition, where the analysis tries to identify any speech within the audio. The term ‘audio mining’ is sometimes used interchangeably with audio indexing, phonetic searching, phonetic indexing, speech indexing, audio analytics, speech analytics, word spotting, and information retrieval. Audio indexing, however, is mostly used to describe the pre-process of audio mining, in which the audio file is broken down into a searchable index of words.
Shazam is an application that can identify music based on a short sample played using the microphone on the device. It was created by the British company Shazam Entertainment, based in London, and has been owned by Apple Inc. since 2018. The software is available for Android, macOS, iOS, Wear OS, watchOS and as a Google Chrome extension.
Munax was a Swedish company that developed a Large Hyper-Parallel Execution (LHPE) search engine system Munax XE. Munax XE, is an all-content search engine and powered nationwide and worldwide public search engines with page, document, audio, video, images, software, and email search. Other customers included vertical search engines and mobile operators.
TinEye is a reverse image search engine developed and offered by Idée, Inc., a company based in Toronto, Ontario, Canada. It is the first image search engine on the web to use image identification technology rather than keywords, metadata or watermarks. TinEye allows users to search not using keywords but with images. Upon submitting an image, TinEye creates a "unique and compact digital signature or fingerprint" of the image and matches it with other indexed images. This procedure is able to match even heavily edited versions of the submitted image, but will not usually return similar images in the results.
Tunebot is a music search engine developed by the Interactive Audio Lab at Northwestern University. Users can search the database by humming or singing a melody into a microphone, playing the melody on a virtual keyboard, or by typing some of the lyrics. This allows users to finally identify that song that was stuck in their head.
Reverse image search is a content-based image retrieval (CBIR) query technique that involves providing the CBIR system with a sample image that it will then base its search upon; in terms of information retrieval, the sample image is very useful. In particular, reverse image search is characterized by a lack of search terms. This effectively removes the need for a user to guess at keywords or terms that may or may not return a correct result. Reverse image search also allows users to discover content that is related to a specific sample image or the popularity of an image, and to discover manipulated versions and derivative works.
Discoverability is the degree to which something, especially a piece of content or information, can be found in a search of a file, database, or other information system. Discoverability is a concern in library and information science, many aspects of digital media, software and web development, and in marketing, since products and services cannot be used if people cannot find it or do not understand what it can be used for.
An acoustic fingerprint is a condensed digital summary, a fingerprint, deterministically generated from an audio signal, that can be used to identify an audio sample or quickly locate similar items in an audio database.
The following outline is provided as an overview of and topical guide to search engines.
Search by sound is the retrieval of information based on audio input. There are a handful of applications, specifically for mobile devices that utilize search by sound. Shazam, Soundhound, Axwave, ACRCloud and others have seen considerable success by using a simple algorithm to match an acoustic fingerprint to a song in a library. These applications take a sample clip of a song, or a user-generated melody and check a music library/music database to see where the clip matches with the song. From there, song information will be queried and displayed to the user.
Automatic content recognition (ACR) is a technology used to identify content played on a media device or presented within a media file. Devices with ACR can allow for the collection of content consumption information automatically at the screen level itself, without any user-based input or search efforts. This information may be collected for purposes such as personalized advertising, content recommendations, sale to customer data aggregators and other applications.