Identifying and generating audio cohorts based on audio data input

A computer implemented method, apparatus, and computer program product for generating audio cohorts. An audio analysis engine receives audio data from a set of audio input devices. The audio data is associated with a plurality of objects. The audio data comprises a set of audio patterns. The audio data is processed to identify attributes of the audio data to form digital audio data. The digital audio data comprises metadata describing the attributes of the audio data. A set of audio cohorts is generated using the digital audio data and cohort criteria. Each audio cohort in the set of audio cohorts comprises a set of objects from the plurality of objects that share at least one audio attribute in common.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to an improved data processing system and in particular to a method and apparatus for generating cohorts. More particularly, the present invention is directed to a computer implemented method, apparatus, and computer usable program code for processing audio input data to generate cohorts.

2. Description of the Related Art

A cohort is a group of people, animals, plants, places, or objects that share a common attribute or experience. For example, a group of people born in 1980 may form a birth cohort. A cohort may include one or more sub-cohorts. For example, the birth cohort of people born in 1980 may include a sub-cohort of people born in 1980 in Salt Lake City, Utah. A sub-subcohort may include people born in 1980 in Salt Lake City, Utah to low income, single parent households.

Cohorts are frequently generated based on one or more attributes of the members of each cohort. The information used to identify the attributes of members of a cohort is typically provided by the members of the cohort. However, information describing attributes of members of a cohort may be voluminous, dynamically changing, unavailable, difficult to collect, and/or unknown to the member of the cohort and/or the user selecting members of a cohort. Moreover, it may be difficult, time consuming, or impractical for an individual to access all the information necessary to accurately generate cohorts. Thus, unique cohorts may be sub-optimal because individuals lack the skills, time, knowledge, and/or expertise needed to gather cohort attribute information from available sources.

BRIEF SUMMARY OF THE INVENTION

According to one embodiment of the present invention, a computer implemented method, apparatus, and computer program product for generating audio cohorts is provided. An audio analysis engine receives audio data from a set of audio input devices. The audio data is associated with a plurality of objects. The audio data comprises a set of audio patterns. The audio data is processed to identify audio attributes to form digital audio data. The digital audio data comprises metadata describing audio attributes of the audio data. A set of audio cohorts is generated using the audio attributes associated with the digital audio data and cohort criteria. Each audio cohort in the set of audio cohorts comprises a set of objects from the plurality of objects that share at least one audio attribute in common.

DETAILED DESCRIPTION OF THE INVENTION

Program code located in network data processing system100may be stored on a computer recordable storage medium and downloaded to a data processing system or other device for use. For example, program code may be stored on a computer recordable storage medium on server104and downloaded to client110over network102for use on client110.

In some illustrative embodiments, program code216may be downloaded over a network to persistent storage208from another device or data processing system for use within data processing system200. For instance, program code stored in a computer readable storage medium in a server data processing system may be downloaded over a network from the server to data processing system200. The data processing system providing program code216may be a server computer, a client computer, or some other device capable of storing and transmitting program code216.

As one example, a storage device in data processing system200is any hardware apparatus that may store data. Memory206, persistent storage208, and computer readable media218are examples of storage devices in a tangible form.

In many industries and professions, such as, without limitation, search and rescue, research, and industry, it is useful to be able to distinguish different sounds, determine the strength of a sound, and the source of a particular sound. A human user may manually identify sounds and the source of a sound using natural human sense of hearing and/or a human user utilizing a hearing aid or other device that amplifies or modulates sounds for the wearer in combination with the human user's knowledge and experience. However, this technique is limited by the human ability to hear and the human ability to distinguish different sounds in a multitude of different sounds.

Currently, electronic sensing technologies have been developed that are capable of reproducing human senses using audio sensors and other electronic devices, such as microphones. The illustrative embodiments recognize that the information generated by electronic audio sensors may be used to identify cohorts and improve selection of members of cohorts. As used herein, a cohort is a group of objects that share at least one common attribute. The objects in a cohort may be humans, animals, plants, places, locations, or inanimate objects. A cohort may be a null set having no members, a single member, or a group including two or more members. Cohorts may be used in research, marketing, safety studies, and many other industries for various uses.

Therefore, in one embodiment of the present invention, a computer implemented method, apparatus, and computer program product for generating audio cohorts is provided. An audio analysis engine receives audio data from a set of audio input devices. An audio input device is any type of device that can capture, detect, measure, or record vibrations and sound waves transmitted through solids, liquids, semi-solids, or gases, such as, without limitation, the air. For example, an audio input device may be implemented as, but without limitation, a microphone or a sonar device. The audio data may be in an analog format. The audio data is associated with one or more objects. The audio data may include a set of audio patterns. As used herein, the term “set” refers to one or more, unless specifically defined otherwise. Thus, a set of audio patterns comprises one or more audio patterns. An audio pattern may be any type of pattern, such as, without limitation, patterns in frequency, wavelength, speed, direction, volume, intensity, or any other attribute in audio data.

An audio analysis engine analyzes and processes the audio data and any audio patterns in the audio data to generate digital audio data. The digital audio data comprises metadata describing the audio attributes of the audio data. The audio analysis engine may utilize any type of known or available audio analysis software, audio spectrum analyzer, bioacoustic analysis software, and/or voice recognition software, also referred to as speech recognition software.

An audio attribute is a characteristic, feature, property, character, or quality of a human, an animal, a plant, an object, a place, or a location. For example, audio attributes may include an identification of a sound, identification of a type of sound, a source or origin of a sound, identification of an object generating a sound, identification of a combination of sounds, identification of a combination of objects generating a sound or a combination of sounds, a volume of a sound, and sound wave properties.

A sound wave property is a measurable property of a sound. Sound wave properties include, without limitation, the frequency, wavelength, amplitude, intensity, speed, and direction of a sound. The type of sound may be a general classification or sub-classification of a sound. A type of sound may include, without limitation, a human voice talking, an animal sound, an engine sound, a baby cry, a door opening or closing, footsteps, water running, rain, sound of breaking or shattering, laughter, or any other types of sounds. A sub-category of a type of sound may be more specific. For example, and without limitation, the classification of a human voice talking may have sub-classifications of a female voice talking, a male voice talking, a human voice talking in a whisper, a human voice talking loudly, a human voice talking with a southern accent, a human voice talking with a Texas accent, a human voice talking with a Scottish brogue, a human voice talking with a particular dialect or regional accent, talking in English, talking in Spanish, or talking in any other language.

An identification of a sound is an identification of a particular sound. For example, an identification of a sound may be the sound of a Harley motorcycle engine, a specific, identified, person's voice, a particular door opening or closing, or some other specific, identified sound. For example, a sound may be identified generally as a human voice, specifically as an identified person's voice, human speech, or specific content of the human speech. Still more particularly, the identification of a sound may include an identification of the sound as human speech and an identification of attributes of the human speech. Attributes of human speech may include, without limitation, an identification of at least one of a language spoken, a regional dialect associated with the human speech, an accent associated with the human speech, an identification of whether the speaker is male or female, a rapidity of speech, and an identification of words spoken in the human speech or the subject matter/content of the speech. The phrase “at least one” means that the attributes of human speech may include only a single one of the listed attributes, as well as any combination of two or more of the attributes of human speech. For example, the attributes may include an identification of the speaker as male. In another example, the attributes may identify the speaker as a male speaker that is speaking English. In yet another example, the attributes may identify the speaker as a female speaking Spanish with a Venezuelan dialect.

A set of audio cohorts is generated using the attributes in the digital audio data and cohort criteria. Cohort criteria are rules, standards, guidelines, or other criteria for selecting members of a cohort. For example, and without limitation, cohort criteria may specify that members of a car cohort should only include cars with engines that generate noises below a certain loudness threshold.

The set of audio cohorts may comprise a single cohort, as well as two or more cohorts. An audio cohort may have any number of members from a null set to an infinite number of members. An audio cohort may include members that are sounds or audio information. However, audio cohorts are not limited to sounds or audio information describing sounds. A member of an audio cohort may include a human, animal, plant, place, location, or inanimate object. In other words, an audio cohort is a cohort with member objects that are selected using audio data and attributes of audio data. In one embodiment, the audio cohort may be used in a system wide monitoring process to quickly and efficiently pass vital information to a real-time computational process. Thus, the embodiments permit a user to create cohorts based on audio data describing different sounds, sources of different sounds, and other attributes of one or more sounds in a particular area or environment.

For example, and without limitation, a user may wish to identify every customer in a shopping mall that is speaking to a sale representative or speaking to someone in customer service. In such a case, the user may analyze digital audio data to identify customers that are associated with conversations taking place at customer service centers and/or customers speaking to employees prior to making a purchase. These customers may then be selected as members of one or more cohorts of customers utilizing employee expertise or otherwise utilizing customer services provided by employees.

In another example, attributes of the audio data may be used to identify sounds made by animals and sounds made by babies or children. In such a case, the attributes in the audio data may be used to identify customers with a child or customers with a pet. This audio data may then be used to create cohorts of children, cohorts of pets, cohorts of dogs, cohorts of babies, cohorts of toddlers, cohorts of young children, cohorts of parents or guardians with children, and/or cohorts of pet owners with their pets. These cohorts provide an effective tool for users to create cohorts based on an analysis of audio sensory input gathered by audio input devices, such as, without limitation, a microphone.

FIG. 3is a block diagram of an audio sensory input analysis system in accordance with an illustrative embodiment. Computer300may be implemented using any type of computing device, such as, but not limited to, a main frame, server, a personal computer, laptop, personal digital assistant (PDA), or any other computing device depicted inFIGS. 1 and 2.

Set of audio sensors302is a set of audio input devices that detect, capture, and/or record vibrations304, such as, without limitation, pressure waves and sound waves. Vibrations304may be detected as vibrations304are transmitted through a solid object, a liquid, a semisolid, or a gas, such as the air or atmosphere. Set of audio sensors302may include only a single audio input device, as well as two or more audio input devices. An audio sensor in set of audio sensors302may be implemented as any type of device, such as, without limitation, a microphone, a sonar device, an acoustic identification system, or any other device capable of detecting vibrations304transmitted through a medium, such as a solid, liquid, semisolid, and/or gas.

In this embodiment, set of audio sensors302is implemented as a set of microphones connected to computer300, which detects and records sounds and identifies patterns of sounds in vibrations304. However, set of audio sensors302is not limited to implementation using only microphones.

Set of audio sensors302analyzes vibrations304in the audio sample to generate audio data306. In this example, audio data306is analog data generated by set of audio sensors302in response to detecting vibrations304. However, the embodiments are not limited to audio data in an analog format. The embodiments may also be implemented using a set of audio sensors that generate audio data in a digital format.

Audio data306may include audio patterns in vibrations304identified by set of audio sensors302. An audio pattern is a pattern of data that describes patterns or changes the properties of sound waves that occur over time. Different audio patterns are created as a result of contact or exposure with different sounds, different volumes of sounds, different environments which may amplify, echo, or absorb sound, and/or different combinations of sounds in a particular environment.

For example, a particular audio pattern is produced when a dog barks and a different audio pattern is produced when an engine is revved up. Likewise, a dog barking outside in an open area with few obstructions will produce one audio pattern, while a dog barking in an enclosed space that tends to cause sound to bounce of one or more surfaces and echo sound back will produce a different audio pattern. In another example, if two dogs are barking at a cat that is meowing, a different audio pattern is produced than when only a single dog is barking in an empty room with no other sources of sound present. In other words, a unique audio pattern is produced in response to each unique type of sound, origin of sound, location or environment where the sound is detected, combination of different sounds, and/or volume of the sound.

Audio data306is generated in response to set of audio sensors302detecting a particular sound and/or combinations of sounds in a single audio sample or a series of audio samples. An audio sample comprises the vibrations that are detected and/or recorded during a particular time period. For example, and without limitation, a microphone may record sounds for ten minutes. In such a case, the ten minutes of recording may be a single audio sample or the ten minutes of recording may be broken up into thirty (30) second audio samples to create twenty (20) audio samples from the ten minutes of recording. However, an audio sample is not limited to ten minute or thirty second time periods. An audio recording may be broken down into any number of time segments, such as, without limitation, hours, minutes, seconds, milliseconds, microseconds, or any other units of time. An audio recording may also be broken down into a single audio sample, as well as two or more audio samples.

In addition, the embodiments do not require that an audio recording be separated into audio samples. An audio recording may be analyzed as a single recording in which a particular segment of the recording is identified or located using time indicators or any other indicators. For example, in an hour long audio recording, indicators may be used to identify a particular segment of the audio recording that occurs twenty minutes (20) and twenty (20) seconds into the recording, or the segment may be identified using the time. So if the segment occurring twenty minutes and twenty seconds into the recording were recorded from 3:45 p.m. to 3:46 p.m., that segment may be identified and located using the time. In another example, a particular segment may be identified using an identification number, an index, or any other method for identifying portions of an audio recording.

Set of audio sensors302sends audio data306to audio analysis engine308in computer300. If audio data302is in an analog format, audio analysis engine308converts audio data306from the analog format into a digital format that is compatible with cohort generation engine314. In other words, audio analysis engine308processes audio data306to form digital audio data312. Digital audio data312identifies attributes of vibrations304. Digital audio data312comprises metadata describing the identified attributes of vibrations304sounds in the audio sample used to generate digital audio data312. Metadata generator310is a software component for generating the metadata describing the attributes of vibrations304. The metadata in digital audio data312describes attributes of vibrations304detected in an audio sample. For example, and without limitation, attributes in digital audio data312may identify a source or origin of a sound, the type of sound, identify a particular sound, identify a combination of sounds, and/or any other attributes. Digital audio data312may be transmitted to cohort generation engine314as, without limitation, a digital wave file with the metadata.

Digital audio data312may also be used to identify humans and animals with irregular breathing, wheezing, congestion, coughing, or sneezing to identify, for example and without limitation, patients and/or animals that are suffering from infections, bronchitis, asthma, the flu, a cold or other health problems, the sound of car alarms, gas leaks, tornado sirens, smoke alarms, fire alarms, burglar alarms, police sirens, fire truck sirens, ambulance sirens, and other emergency or warning alarms that may indicate the presence of potentially hazardous conditions, emergency situations, and/or dangerous substances.

Cohort generation engine314receives digital audio data312from audio analysis engine308. Cohort generation engine314may request digital audio data312from audio analysis engine308. In another embodiment, audio analysis engine308automatically sends digital audio data312to cohort generation engine314in real time as digital audio data312is generated. In yet another embodiment, audio analysis engine308sends digital audio data312to cohort generation engine314upon the occurrence of a predetermined event, such as, without limitation, a predetermined time, completion of processing audio data306, occurrence of a timeout event, a user request for generation of set of audio cohorts324based on digital audio data312, or any other predetermined event.

Cohort generation engine314generates set of audio cohorts324based on digital audio data312. In one embodiment, cohort generation engine314compares the audio patterns described in digital audio data312with identified audio patterns316for comparison. Each different sound and/or combination of sounds in a particular environment creates a different audio pattern. Audio patterns316are audio patterns for known sounds and/or combinations of sounds in a particular environment. A particular audio pattern may be compared to known audio patterns. If a match is found, the matching audio pattern may be used to identify attributes of a particular sound associated with vibrations304.

Cohort generation engine314may optionally process digital audio data312in data models320. Data models320are a set of one or more data models for processing audio data and identifying attributes of sounds associated with vibrations304. A data model is a model for structuring, defining, organizing, imposing limitations or constraints, and/or otherwise manipulating data and metadata to produce a result. A data model may be generated using any type of modeling method or simulation including, but not limited to, a statistical method, a data mining method, a causal model, a mathematical model, a marketing model, a behavioral model, a psychological model, a sociological model, or a simulation model. In one embodiment, digital audio data312is processed using one or more data models to create one or more audio cohorts. For example, a data model may be used to identify customers in a department store that are not speaking English. This information is used to create a cohort of English speaking customers and/or cohorts of non-English speaking customers. Likewise, the audio data may be processed in a data model to identify customers speaking a particular language, such as Spanish, German, Mandarin, or any other language. This information may be used to create a cohort of Spanish speaking customers, a cohort of Mandarin speaking customers, and so forth. This information may be useful, for example and without limitation, where a store is providing customer service or employees that speak Spanish, Mandarin, or other languages to better serve their customers.

Cohort generation engine314generates set of audio cohorts using cohort criteria322and optionally, the results of processing digital audio data312in data models320and/or comparisons of digital audio data312with known audio patterns316.

Cohort criteria322are a set of criteria and/or guidelines for generating set of audio cohorts324. Cohort criteria322may include, without limitation, sound wave properties, origin of a sound, identification of a sound, and/or identification of a combination of sounds. The sound wave properties may include, without limitation, frequency, volume, amplitude, pitch, wavelength, intensity, direction, and/or speed. Other cohort criteria may include, without limitation, dialect, regional accents, and/or the content of human conversation. The content of human conversation refers to the actual words spoken by a human or the subject matter of a conversation. The audio of a human conversation may be converted into data describing the content of the conversation using any type of known or available speech recognition software. The cohort criteria specify the criteria used to select members for a cohort. For example, and without limitation, cohort criteria may specify that a particular cohort should include all cars with engine producing sounds above a certain volume or decibel level.

Cohort generation engine314may also optionally utilize cohort constraints (not shown) to generate set of audio cohorts324. Cohort constraints specify attributes that are absent from members of a given cohort. In other words, cohort constraints specify attributes that are not present or that are not associated with the members of a particular cohort group. This is in contrast to cohort criteria which specify attributes that are required for members of a particular cohort.

Set of audio cohorts324is a set of one or more cohorts. Each cohort comprises a set having null to infinity member. In other words, an audio cohort may have no members, a single member, or two or more members that share a common attribute. A member of a cohort may be a human, an animal, a plant, place, thing, or object.

For example, if digital audio data312indicates the sounds of footsteps along a jogging path and one or more dogs barking in a park, the digital audio data312may be used to generate cohorts of pet owners walking their dogs in the park. Digital audio data312may be used to identify cohorts of patients in a hospital with symptoms of coughing, congestion, labored breathing, and/or abnormal breath sounds. Digital audio data312may be used to create a cohort of patients having irregular or labored breathing and/or cohorts of patients with regular, unlabored breathing. In yet another example, digital audio data312may be used to identify cars, motorcycles, trucks, and other motor vehicles with irregular engine firing indicating the vehicle may need a tune-up or new spark plugs or other mechanical problems that may be identified based on sounds made by the vehicle while being started, idling, driving, changing gears, or other operation of the vehicle. The information may be used to create cohorts of vehicle that need a tune-up, new spark plugs, a new starter, or other maintenance and repair work. Thus, the audio cohorts may be used for a variety of industrial, medical, health, safety, and research related endeavors. The audio cohorts may be used as a set of methodologies to solve a variety of problems.

In one embodiment, cohort generation engine314provides set of audio cohorts324to inference engine326. Inference engine326is a computer program that derives inferences from a knowledge base. In this example, inference engine326derives inferences328from set of audio cohorts324and/or any other data available in the knowledge base. The data in the knowledge base may include data located in a database located locally on computer300as well as data located on one or more remote data storage devices that may be accessed using a network connection, such as data storage device318. Data storage device318is any type of device for storing data, such as, without limitation, storage108inFIG. 1.

Inferences are conclusions regarding possible future events or future changes in the attributes of cohorts that are drawn or inferred based on the knowledge base and set of audio cohorts324. For example, and without limitation, set of audio cohorts324may be analyzed by inference engine326to determine whether a particular volcano is about to erupt, an approximate time when the volcano may erupt, and/or a duration of the potential eruption. In another example, inference engine326may generate inferences regarding which customers are likely to purchase particular products based on cohorts of customers using those products.

In this embodiment, set of chemical sensors302is implemented as a separate device than computer300. However, in another embodiment, set of chemical sensors302and audio pattern analysis308in computer300may be embodied within a single device.

Turning now toFIG. 4, a block diagram of an audio sensor for capturing audio sensory input is shown in accordance with an illustrative embodiment. Set of audio sensors400is a set of device for detecting and/or recording vibrations402moving through a medium, such as a solid, liquid, semi-solid, or gas. Set of audio sensors400may include microphone404. Microphone404is a device that converts sound to an electric signal. Microphone404may be implemented using one or more microphones. Microphone404may be a wired microphone or a wireless microphone. Microphone404device that detects vibrations402moving through a medium, such as a solid, liquid, semi-solid, or gas. Geophone406is a device that detects vibrations402moving through the ground or soil. Hydrophone408is a device that detects vibrations402moving through water or other liquids.

Sonar/Sodar409is a sound navigation and ranging (SONAR) and/or sound detection and ranging (SODAR) device that detects the presence of people, animals, plants, and objects using variations in sound speed and echo ranging. Sonar/Sodar409may be a passive device that passively receives vibrations402. Sonar/Sodar409may also be implemented using an active sonar/sodar device that transmits a pulse and then uses a receiver to receive reflections of the pulse bouncing off objects. Set of audio sensors400generates audio data410in an analog format in response to detecting vibrations402.

Set of audio sensors400is not limited to including one or more microphones, geophones, hydrophones, sonars, and/or sodars. Set of audio sensors400may include any combination of microphones, geophones, hydrophones, sonars, and/or sodars. Set of audio sensors400may include only microphones. In another example, set of audio sensors400may include both microphones and sonar devices. Likewise, set of audio sensors400may include other devices for detecting vibrations402that are not shown inFIG. 4. For example, set of audio sensors400may include only an acoustic location device that detects vibrations402and determines a distance and direction of an object based on vibrations402. Thus, set of audio sensors400may include any combination of audio sensors capable of detecting vibrations402and generating audio data410in response to vibrations402.

FIG. 5is a block diagram of digital audio data generated based on results of processing audio data in accordance with an illustrative embodiment. Digital audio data500is digital data comprises metadata describing the attributes of audio data, such as, but without limitation, sound wave properties502, a time of detection504of the sound waves, rate of change of sound wave properties506, location of the audio sensor508, origin of the sound waves510or origin of the person, animal, plant, or object generating the sound waves, identification of the sound512and/or identification of the type of sound514. The type of sound may include, without limitation, animal sounds, engine sounds, bicycle sounds, human voice or human speech sounds, whether human speech sounds are being generated by a males speaker or a female speaker, the language being spoken, the content of the human speech, and/or whether human speech or words are being spoken with a dialect or regional accent, such as, without limitation, a Scottish brogue or a Southern accent.

The embodiments are not limited to the audio attributes shown in digital audio data500. For example, audio attributes may also include the volume of a sound, the distortion, interference, timbre, brightness of a sound, and any other properties of sound.

Referring now toFIG. 6, a flowchart of a process for generating audio data in accordance with an illustrative embodiment. The process inFIG. 6may be implemented by a set of one or more audio sensors, such as set of audio sensors302inFIG. 3.

The process begins by making a determination as to whether any sound is detected by a set of audio sensors (step602). If sound is detected, the set of audio sensors generates an audio pattern in the audio data in response to detecting the sound (step604). The set of audio sensors sends the audio data with the audio patterns to an audio analysis engine (step606).

Returning to step602, if no sound is detected or after sending the audio data to the audio analysis engine in step606, a determination is made as to whether more sound is detected by the set of audio sensors (step608). If more sounds are not detected, the process terminates.

Returning to step608, if a determination is made that more sounds are detected, the set of audio sensors generate new audio data (step610) based on the new sounds detected by the set of audio sensors. The set of audio sensors then returns to step602and implements steps602-610iteratively until no new air samples are taken at step608and the process terminates thereafter.

Turning now toFIG. 7, a flowchart of a process for generating cohorts based on audio sensory input is shown in accordance with an illustrative embodiment. The process inFIG. 7may be implemented by software for processing audio data, such as audio analysis engine308inFIG. 3. Step706may be implemented by software for generating cohorts groups based on digital audio data, such as cohort generation engine314inFIG. 3.

The process begins by receiving audio data from a set of audio sensors (step702). The audio analysis engine processes and parses the audio data to form digital audio data describing attributes of the audio data (step704). The cohort generation engine generates a set of audio cohorts using the digital audio data and cohort criteria, such as cohort criteria322inFIG. 3(step706) with the process terminating thereafter. As discussed above, the cohort generation engine is not limited to using only the digital audio data and cohort criteria. The cohort generation engine may also optionally utilize cohort constraints, data models, identified audio patterns, and any other available analysis tools and information to select members of a particular cohort group based on available audio data and audio attributes.

Thus, in one embodiment, a computer implemented method, apparatus, and computer program product for generating audio cohorts is provided. An audio analysis engine receives audio data from a set of audio input devices. The audio data is in an analog format. The audio data identifies a set of audio patterns. The audio data is processed and attributes of the audio data are identified to form digital audio data. The digital audio data comprises metadata describing the attributes of the audio data. A set of audio cohorts is generated using the digital audio data.

The cohort generation engine may be used to identify cohorts based on types of sounds, identification of sounds, contents of speech, languages spoken, combinations of sounds, the strength and direction of a sound, the volume of a sound, and other properties of sounds. The audio analysis engine may be used, without limitation, to identify illnesses based on sounds such as coughing, sneezing, wheezing breath, rattling in the chest, and other sounds. The audio analysis engine may also be used to identify particular people based on the sound of someone's voice, their accent or dialect, and other speech attributes. Audio attributes may also be used to identify cars, motorcycles, and machines based on the sounds of engines and machine parts, identify animals based on barking, meowing, and other animal articulations, and identify objects based on sounds made by the objects when the objects move or are acted upon by an outside force. The audio cohorts may be used in a variety of industries for a variety of purposes such as, for example and without limitation, in sales, marketing, marketing research, medical and diagnostic research, public health, demographic research, marketing trends, safety and/or security. The olfactory cohorts may also be used for quality control in the food industry, in pharmaceuticals, manufacturing, diagnostics, and other areas.