OCS Study

BOEM 2015-056

U.S. Department of the Interior 

Bureau of Ocean Energy Management

Acoustic Metadata Management and Transparent 

Access to Networked Oceanographic Data Sets

NOAA Permit #727-1915
US Department of the Interior

Bureau of Ocean Energy Management

ACOUSTIC METADATA MANAGEMENT AND TRANSPARENT 

ACCESS TO NETWORKED OCEANOGRAPHIC DATA SETS

Authors

Marie A. Roch

Simone Baumann-Pickering

Catherine L. Berchok

John A. Hildebrand

Erin M. Oleson

Sofie M. Van Parijs

Prepared under cooperative agreement

National Oceanographic Partnership Program

BOEM Award #: M11PG00012

Office of Naval Research Award #: N00014-11-1-0697

Published by
iii

CONTENTS

1. INTRODUCTION ....................................................................................................................................3

2. BACKGROUND.......................................................................................................................................3

3. METHODS................................................................................................................................................4

TETHYS SCHEMATA............................................................................................................................................4

TETHYS REFERENCE IMPLEMENTATION ...........................................................................................................10

4. RESULTS................................................................................................................................................11

5. DISCUSSION..........................................................................................................................................11

6. REFERENCES .......................................................................................................................................12

List of Figures

Figure 1 - A network data model provides linkages between different types of data.. .................. 5

Figure 2 – Top level view of the schema for a detections document. ............................................ 7

Figure 3 – Schema for individual detections. ................................................................................. 9

Figure 4 – Tethys reference implementation architecture ............................................................ 11

Figure 5 – Automated detections of blue whale B calls (left) and echolocation clicks from 

odontocetes (right) over multiple years. ................................................................... 12

List of Tables

Table 1 – Tethys schemata categories. ........................................................................................... 6

Abbreviations and Acronyms

BOEM Bureau of Ocean Energy Management

NASA National Aeronautics and Space Administration 

NOAA National Oceanic and Atmospheric Administration

OBIS-SEAMAP Ocean Biogeographic Observing System – Spatial Ecological Analysis of 

Megavertebrate Populations

XML Extensible markup language

DISCLAIMER

Study collaboration and funding were provided by the US Department of the Interior, Bureau of 

Ocean Energy Management (BOEM), Environmental Studies Program, Washington, DC, under 

Agreement Number N00014-11-1-0697. This report has been technically reviewed by BOEM 

and it has been approved for publication. The views and conclusions contained in this document 

are those of the authors and should not be interpreted as representing the opinions or policies of 

the US Government, nor does mention of trade names or commercial products constitute 

endorsement or recommendation for use.
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REPORT AVAILABILITY

To download a PDF file of this Environmental Studies Program report, go to the US 

Department of the Interior, Bureau of Ocean Energy Management, Environmental Studies 

Program Information System website and search on OCS Study BOEM 2015-056.

CITATION

Roch, M. A, Baumann-Pickering, S., Berchock, C. L., Hildebrand, J. A., Oleson, E. M., and Van 

Parijs, S. M., Acpustic Metadata Management and Transparent Access to Networked 

Oceanographic Data Sets, U.S. Department of the Interior, Bureau of Ocean Energy 

Management. XX pp.

ABOUT THE COVER

Bottlenose dolphins off the coast of San Diego California (NOAA permit #727-1915), photo 

courtesy of Amanda Debich. Deployment of high frequency acoustic response package data 

loggers from the RV Gordon Sproul, photo courtesy of Sean M. Wiggins.
v

ACKNOWLEDGMENTS

The authors wish to thank Jim Price and Jill Lewandowski from BOEM (contract M11PG00012)

as well as Michael Wiese from the Office of Naval Research (award N00014-11-1-0697) for 

their support in bringing this project to fruition. Many people contributed to the success of this 

project. At Duke University, Ei Fujioka, was instrumental in helping us to set up data export to 

OBIS-SEAMAP. NOAA personnel at Northeast Fisheries Science Center (Danielle Cholewiak, 

Dana Gerlach, and Denise Rische) were early adopters of the Tethys and helped us move the 

product towards maturity. Members of Southwest and Southeast Fisheries Science Centers, 

Shannon Rankin and Melissa Soldevilla, as well as Ana Širović of Scripps Institution of 

Oceanography helped us to better define the schemata. Lisa Munger from the NOAA Pacific 

Islands Fisheries Science Center designed early versions of the diel plotting tools and named the 

system while a student at Scripps Institution of Oceanography. We thank the programmers who 

contributed to the code base: Heidi Batchelor, Sean Herbert, and Daniel Hwang, and the many 

analysts at Scripps Institution of Oceanography who contributed data to the early system.
vi

ABSTRACT

Recent expansion in the capabilities of passive acoustic monitoring of sound-producing 

animals is providing expansive data sets in many locations. These long-term data sets will allow 

the investigation of questions related to the ecology of sound-producing animals on time scales 

ranging from diel and seasonal to inter-annual and decadal. Analyses of these data often span 

multiple analysts from various research groups over several years of effort, and as a consequence 

have begun to generate large amounts of scattered acoustic metadata. It has therefore become 

imperative to standardize the types of metadata being generated. A critical aspect of being able to 

learn from such large varied acoustic data sets is providing consistent and transparent access. 

This is juxtaposed with the need to include new information for specific research questions that 

evolve over time. Hence, a method is proposed for organizing acoustic metadata that addresses 

many of the problems associated with the retention of metadata from large passive acoustic data 

sets. 

A structure was developed for organizing acoustic metadata in a standardized manner along 

with a mechanism that implements the data representation into a client-server oriented database. 

The implementation was extended to access Internet-available data products, permitting access to 

a variety of environmental information types (e.g. sea surface temperature, ephemeris, etc.) from 

a wide range of sources as if they were part of the data service. This metadata service is in use at 

several institutions and is used to track and analyze millions of acoustic detections.
3

1. INTRODUCTION

The use of bioacoustics to detect, classify, localize, and establish density estimates of marine 

fauna provides a cost-effective complement or alternative to visual-based methods of study for 

monitoring and mitigation (Mellinger et al., 2007; Marques et al., 2011) and considerable 

resources have been invested in the development of bioacoustic analysis methods to accomplish 

this. As the number of available recordings grows, the ability to manage information derived 

from these recordings (metadata of the recordings) becomes crucial in order to combine data 

across disparate studies to provide information at temporal and spatial scales that are meaningful 

with respect to oceanic, atmospheric, and anthropogenic processes that may affect the health and 

productivity of various animal stocks.

Of particular importance for bioacoustic metadata is the specification of the how the 

metadata were generated. The period over which effort was invested may not be the same as that 

of the acoustic data itself. Examples of this include gaps due to instrument failure, analysis of 

targeted time periods, etc. In addition to specifying the period, the methods used to analyze the 

acoustic data must be documented in a way that permits scientists to make intelligent decisions 

about when acoustic metadata from different studies can be combined and when they should not.

In this report, we provide an overview of a set of metadata structuring rules called the Tethys 

Metadata Schemata that are designed to be consistent yet extensible. Consistency is a clear 

prerequisite to combining information from multiple studies. This must be balanced with the 

ability to record new parameters. A researcher studying a specific call type may realize that 

there are nuances to the call that can be associated with properties such as kinship or other 

meaningful distinctions (e.g. in birds, see Akçay et al., 2014). Consequently, it is necessary to

maintain a balance between consistency and extensibility, and the philosophy of this project is to 

provide structure whenever possible while allowing for new types of information to be stored in 

a manner that can become standardized should their use become widespread.

In order to be useful the metadata structuring rules require software to implement them. We

have developed an implementation called Tethys Metadata Workbench. It is a client-server 

model that permits groups of researchers to install a server program that lets individual users 

store and retrieve acoustic metadata. Tethys metadata servers are currently running at Scripps 

Institution of Oceanography, The National Oceanic and Atmospheric Administration (NOAA), 

and Cornell University. In addition to providing database services, the Tethys metadata server 

also provides access to oceanographic data sets in a consistent manner.

2.BACKGROUND

To date, there is no standard that addresses the needs of acoustic metadata completely, 

although several standards address aspects of it. The International Standards Organization ISO19115 (2003) and Open Geospatial Consortium Open Geospatial Consortium (1994) standards 

for equipment specify standards that are suitable for equipment descriptions, but were never 

designed for bioacoustics applications and are missing necessary descriptions for animal calls 

and detection effort. Geospatial inventories of animals are proposed in the Darwin Core standard 

(Wieczorek et al., 2009; Wieczorek et al., 2012), the marine biota theme (Marine and Coastal 

Spatial Data Subcommittee, 2012) of the Federal Geographic Data Committee (2014), and the 

Ecological Metadata Language (Fegraus et al., 2005), but do not cover effort or methods for 

describing acoustic signals produced by animals. Finally the Integrated Ocean Observing 

System (http://www.ioos.noaa.gov) provides a variety of data formats for noting ocean 
4

measurements. A component of this has been developed for passive acoustic recordings (Guan

et al., 2014), but primarily addresses equipment characteristics as opposed to animal or 

anthropogenic sources within passive acoustic recordings. A final element in this set of data 

representations is the work of Fujioka et al. (2014) which extends the Ocean Biogeographic

Observing System – Spatial Ecological Analysis of Megavertebrate Populations (OBISSEAMAP) to include acoustic data. OBIS-SEAMAP is designed to track summary information 

rather than detailed information such as per call timing, call parameters, detailed localization 

information, etc. 

We propose schemata that are tailored to the needs of bioacousticians. When possible, we 

borrow from the aforementioned standards, at times simplifying their structure when failing to do 

so creates additional work for bioacousticians. The data organization schemata that we propose 

are capable of tracking effort, species, calls and their characteristics, and localization information 

at varying levels of granularity, ranging from detailed per call measurements to broader 

measurements such as presence or call counts in specific time bins.

3.METHODS

TETHYS SCHEMATA

Bioacoustic metadata require context. One might need to know what kind of call was made 

by which species, where and when it was made, what effort was made in making, detecting, or 

localizing the call, the methods used, etc. Frequently, this can be seen as a network of 

heterogeneous data that are related to one another through linkages. One example of this can

occur when a comparing the detection of a tonal call which records a sequence of time and 

frequency parameters as compared to a pulsed call (Figure 1, Roch et al., submitted). While the 

pulsed call would contain some of the same parameters, a chain of time/frequency points would 

be inappropriate and other data might be recorded. This type of data network lends itself well to 

representations that can explicitly capture the network linkages between the data types. While 

the last forty some odd years of data storage has been dominated by relational databases (see 

Codd, 1970 for a discussion of the advantages of relational models), the advent of large, 

heterogeneous datasets has led researchers to consider alternative models of data representation 

(e.g. Chang et al., 2008; Leavitt, 2010).
5

Detection

Date and Time Species Call

Detection

Date and Time Species Call

Detections Instrument

Deployment

Location or

Trackline

Recording

parameters

Effort

Species Interval 

Searched

Parameters

Received Level

Image

Time/

Frequency 

Profile

TimeFreq1 TimeFreq2 TimeFreq... TimeFreqN

Figure 1 - A network data model provides linkages between different types of data..

We use extensible markup language (XML) as a means of representing our metadata. XML 

is a data annotation method that provides a hierarchical encapsulation of data (Connolly et al., 

2007) by enclosing data with structuring elements. Elements are simply pairs of names in angled 

brackets before and after the data with a leading slash on the latter instance:

<Call> boing </Call>

Throughout this report, we italicize element names that appear in the main text, but this is only 

for emphasis and is not required by the XML specification. Elements can be nested:

<Detection><Call> boing </Call><Species>...</Species> </Detection>

and relationships between elements are implied from the nesting structure or via explicit network 

paths that locate specific elements within documents. 

In order to provide consistency between disparate detection, classification, and localization 

efforts, there is a need to provide standardized element names and data formats whenever 

possible. The XML schema specification (Walmsley, 2002) provides a mechanism to do so. We 

provide schemata for several concepts related to bioacoustic data:
6

Schema Description

Deployments Characteristics of instrument deployments: when and 

where they are deployed, how they are configured.

Detections Descriptions of methods and effort to find biotic and 

abiotic signals as well as what was detected and 

characterizations of the detections associated with one or 

more deployments.

Localizations Descriptions of methods to find spatial information (e.g. 

bearing angles, three dimensional location, etc.) from one 

or more hydrophones.

Ensembles Groupings of deployed instruments that can be used 

together in multiple hydrophone applications such as 

beam forming, localization, etc.

TransferFunction Descriptions of acoustic instrument calibrations tied to a 

specific sensor, preamplifier, or instrument.

Table 1 – Tethys schemata categories. 

An important aspect of the XML schema specification is that it can provide for extensibility. 

The Tethys schemata take advantage of this by strategically placing rules that allow for arbitrary 

element trees in certain portions of the XML document. Examples of this include the ability to 

provide new call parameter measurements, specify parameters that are used with detection, 

classification, and localization algorithm specifications, etc.

A complete review of the schemata are beyond the scope of this report and many details can 

be found in the Tethys User Manual available at http://tethys.sdsu.edu as well as in (Roch et al., 

2013; Roch et al., submitted). To provide a sense of the nature of the schemata, we present the 

Detections schema at a high level. 

Regardless of the schema, each document has a top-level enclosing element. For the 

Detections schema (Figure 2, Roch et al., submitted), this is Detections. The stacked squares 

connecting Detections to its children indicate a sequence of elements: Description, DataSource, 

Algorithm, etc. Mandatory elements are denoted by bold lines. The majority of elements 

provide structure for child elements (not shown here), such as a group of elements that describe 

the detection effort. Each element has a data type. With the exception of UserID, which has an 

XML primitive type for alphanumeric data, elements in this figure are Tethys-defined types that 

are defined elsewhere in the schema.

The optional Description element contains children that permit a qualitative description of 

the goals of this detection effort. Description is broken down into children Objectives, Abstract, 

and Method. This is followed by a DataSource element that allows one to uniquely link these 

detections to a specific deployment or set of deployments (denoted in an ensemble document). 

The Algorithm element permits the specification of the methods used for detection with enough 

detail to make the effort reproducible. This includes programs used to detect/localize signals 

(automatically or with analyst assistance), their version, parameter settings, etc.

QualityAssurance contains subelements that specify what quality assurance process was 

conducted (if any) and contact information for the person responsible for the process. Somewhat

related to this, a UserID denotes a user identifier of the person who submitted the detection 

document. 
7

Figure 2 – Top level view of the schema for a detections document. 

Effort permits the specification of the portion of the deployment that was analyzed. It 

contains a Start and End time that must lie within the timespan over which the instrument was 

deployed. A list of Kind elements specifies which species and calls were examined along with a 

specification of the resolution of call annotations. Three granularities of annotation are allowed:

1. call – Each call is recorded individually.

2. binned – A time interval is specified in minutes, and detections must fall within 

specific bins. This is usually used for presence/absence, but the number of calls 

present within a bin can be recorded if so desired.

3. encounter – Detections record acoustic encounters. The start time denotes when the 

animals were first detected acoustically and the end time indicates when they are no 

longer producing sound or are no longer within the detection range.
8

The last two elements, OnEffort and OffEffort are very similar. Both permit the specification 

of detections and recording of parameters associated with the detection. Each contain sequences 

of Detection elements (Figure 3, Roch et al., submitted). Detections in the OnEffort element 

must correspond to species, calls, time periods, and granularities specified in the Effort element. 

The OffEffort element permits the notation of interesting calls or phenomena that were not 

searched for systematically. 

Children of Detection include elements such as the Start and End times of the call, bin, or 

encounter, a species identifier from the Integrated Taxonomic Information System (ITIS 

Organization, 2014), and parameters that describe the call including any user defined ones.
9

Figure 3 – Schema for individual detections. 
10

TETHYS REFERENCE IMPLEMENTATION

The Tethys schemata are open to the public and can be implemented by any vendor. We 

provide a supported open source reference implementation that is freely available and runs on the 

Windows operating system (Microsoft Corporation, Redmond WA). Much of the system is 

portable and could be run on other operating systems with little effort, but the data import 

module relies heavily on Microsoft-specific technologies for processing Excel spreadsheets and 

Access databases.

The architecture (Figure 4) is based on a client-server model implementing the RESTful 

model (Fielding, 2000) which relies on a simple set of http protocol (web) operations between 

client and server that do not require the server to retain information about a client’s state. The 

server’s controller and data transport modules are implemented in Python using the open source

CherryPy web framework (CherryPy Team, 2014). A security module permits the use of 

encrypted data transmission between the server and client programs.

Acoustic metadata are stored using the Berkeley dbxml product, an open source XML 

database maintained by Oracle Corporation (Redwood Shores, CA). While other database 

vendors were considered, Berkeley dbxml provided a good combination of performance 

(Manegold, 2008) balanced with the stability of a large and well known leader developer of 

databases. Like most XML databases, the XQuery language (Walmsley, 2006) is used to 

interrogate the database through the RESTful network interface.

Clients were developed to query and import data from a variety of languages: Matlab, Java, 

and Python. A primitive R client has been developed, but development effort focused on other 

areas in which the user community were more interested such as more sophisticated methods of 

importing data and the addition of quality assurance support. The Matlab client implements 

methods to enable users to query the database without learning XQuery and provides several 

visualization tools.

While unrelated to the Tethys schemata, the server implementation supports an architecture 

for importing data from other sources and providing it back to the user in XML as if it were part 

of a Tethys database. These modules mediate between Tethys and other data services, and two 

mediators have been implemented:

1. Ephemeris server – A mediator permits the retrieval of sun and moon positions, 

illumination, sun/moon rise/set events, etc. The mediator connects to The National 

Aeronautics and Space Administration (NASA) Jet Propulsion Lab’s Horizons 

service (Giorgini et al., 1996).

2. Oceanographic server – A mediator interfaces with The NOAA Environmental 

Research Division’s Data Access Program (Simons, 2011) which enables access to a 

wide variety of data products such as the NOAA Tropical Atmospheric Ocean buoys 

or NASA’s Ocean Color.

Data can be added to the database from a wide variety of sources such as spreadsheets, XML 

documents, or database queries. Translation is accomplished via an XML specification that 

maps field names from the user’s analysis tool to those expected by Tethys. When pulling from 

databases, sophisticated queries are possible that include referencing the context of an earlier 

query. For users developing new tools, an application programming interface is provided that 

can permit the generation of XML documents directly, thus skipping the need for translation.
11

 

Berkeley DBXML

Mediators Database

Interface

Transport

Layer

Security

Model Controller

Data Import

Services Clients

Tethys interface

Matlab

Java

Python

R

OBIS-SEAMAP

NASA JPL Horizons

NOAA SWFSC 

ERDDAP

Web

Services

Figure 4 – Tethys reference implementation architecture

4.RESULTS

Tethys servers are used at Scripps Institution of Oceanography, NOAA, and Cornell 

University. The oldest server is at Scripps, and has over five million detections registered. 

Tethys has been used in the generation of US Navy Reports (see reports from 2013-2015 on 

United States naval range complexes at http://cetus.ucsd.edu/reports.html), and has been a 

critical infrastructure component in several recently published articles (Baumann-Pickering et 

al., 2014; Roch et al., 2015; Širović et al., 2015), both using Tethys’s ability to organize 

acoustic metadata and its capacity to help automate the access of environmental data. 

5.DISCUSSION

The Tethys Metadata Workbench has been used to represent over 300 years of detection 

effort across multiple species and many deployments, recording millions of detections in the labs 

of the authors. Visualization capabilities permit the exploratory data visualization, at times 

making patterns or the lack thereof easy to detect. An example can be seen in the plots below 

(Figure 5) which show several years of detections for blue whale (Balaenoptera musculus) D 

calls and odontocete echolocation clicks recorded in the Southern California Bight. The light 

blue shaded region on both plots shows periods where there was no acoustic effort. The dark 
12

blue lines show periods with call activity. In the right plot, a gray hourglass pattern denotes the 

night, making the diel activity pattern associated with foraging on vertical migrators during the 

night very apparent. This figure was produced by an experienced Tethys user with about five 

minutes of programming effort and a few minutes of run time for each plot. Alternative 

visualizations for other questions (e.g. seasonality) are available and raw event times can be 

returned for statistical analyses.

Figure 5 – Automated detections of blue whale B calls (left) and echolocation clicks from odontocetes (right) over multiple 

years.

In addition to helping scientists organize data for analysis, the Tethys Metadata Schema can 

be used to address reporting requirements. NOAA and The National Centers for Environmental 

Information (former National Geographic Data Center branch) are currently trialing the use of 

Tethys to meet their public access to research results requirements.

The flexibility of the Tethys schemata can be seen in a recent application by Peter Wrege and 

Sara Keen at Cornell’s Bioacoustics Research Program. While the schemata were designed with 

marine species in mind, they have successfully used Tethys for analyzing detections of forest 

elephant calls, Loxondonta cyclotis (personal commun. Sara Keen).

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