# Chapter 13: Assembling Scores¶

In the previous chapter we learned how to extract parts and passages from Humdrum files. In this chapter we discuss the reverse procedures: how to assemble and coordinate larger documents from individual segments and parts. We will discuss four tools: the UNIX cat command, and the Humdrum assemble, timebase and rid commands.

## The cat Command¶

The UNIX cat command allows two or more inputs to be concatenated together. If we concatenate two files, the output will consist of the contents of the first file, followed immediately by the contents of the second file. For example, the following command will concatenate together the files mov.1, mov.2 and mov.3 and place the result in the file  complete. The order of concatenation is the same as the order of file names given on the command line:

cat mov.1 mov.2 mov.3 > complete


If each of the concatenated files conforms to the Humdrum syntax, then the resulting combined file is guaranteed to conform to the Humdrum syntax. However, in many cases, there may be redundant information present in the concatenated output. Suppose we had three files, each of which encoded a single measure of music. Concatenating them together might result in an output such as the following:

!! File 1
**kern  **kern  **kern  **kern
*M4/4   *M4/4   *M4/4   *M4/4
*G:     *G:     *G:     *G:
*k[f#]  *k[f#]  *k[f#]  *k[f#]
4GG     4B      4d      4g
=1      =1      =1      =1
4GG     4d      4g      4b
4FF#    4d      [4a     4dd
8GG     4d      8a]     4b
8BB     .       [8g     .
4D      4d      8g]     4a
.       .       8f#     .
*-      *-      *-      *-
!! File 2
**kern  **kern  **kern  **kern
*M4/4   *M4/4   *M4/4   *M4/4
*k[f#]  *k[f#]  *k[f#]  *k[f#]
=2      =2      =2      =2
8G      4d      4g      4b
8F#     .       .       .
4E      4e      4g      4cc#
4D;     4A;     4f#;    4dd;
8A      4cn     4a      4ee
8G      .       .       .
*-      *-      *-      *-
!! File 3
**kern  **kern  **kern  **kern
*M4/4   *M4/4   *M4/4   *M4/4
*k[f#]  *k[f#]  *k[f#]  *k[f#]
*k[f#]  *k[f#]  *k[f#]  *k[f#]
=3      =3      =3      =3
4F#     4d      [4a     8dd
.       .       .       8cc
4G      4d      8a]     4b
.       .       [8g     .
8C#     8e      8g]     4a
8D#     4B      8f#     .
8E      .       8e      8g
8F#     8A      8d      8a
*-      *-      *-      *-


Notice that each complete measure ends with spine-path terminators and that the **kern exclusive interpretations are repeated. This organization has a number of repercussions for various Humdrum tools. For example, the mint command calculates melodic intervals between successive notes within a spine. However, mint will not calculate intervals between pitches that are separated by a spine-path terminator. In other words, in the above output, mint will fail to calculate the melodic intervals between notes in successive measures.

## The rid Command¶

This problem can be resolved by using the Humdrum rid command. The rid command can be used to eliminate various kinds of records. Each option for rid eliminates a different class of records. Here are the record classes with their associated options:

> -Geliminate all global comments > -geliminate only global comments that are empty > -Leliminate all local comments > -leliminate only local comments that are empty > -Ieliminate all interpretations > -ieliminate only null interpretations > -Deliminate all data records > -deliminate only null data records > -Teliminate all tandem interpretations > -teliminate duplicate (repeated) tandem interpretations > -Ueliminate unnecessary exclusive interpretations (see below) > -usame as -U

Null records are devoid of content. For example, null interpretations consist of a single asterisk in each spine; null data record consists of just null data tokens (.) in each spine; null local comments consist of a single exclamation mark in each spine. Null global comments contain just two exclamation marks at the beginning of a record.

Upper- and lower-case options are used to distinguish all records of a certain class (upper-case) from empty, null or repeated records of a certain class (lower-case).

By way of example, the following command will eliminate all global comments (including reference records) from the input:

rid -G Saint-Saens


Similarly, the following command will eliminate all tandem interpretations from an input:

rid -T Vaughan-Williams


Options can be combined. The following command eliminates all global and local comments, interpretations, and null data records:

rid -GLId


The option combination -GLId is very common with rid since only non- null data records are retained in the output.

With the -u option, rid will remove “unnecessary” exclusive interpretations. Exclusive interpretations are deemed unnecessary if they don’t change the current status of the data. In the following example, the second psaltery interpretation is redundant. The rid -u command would remove the first spine-path terminator and the second exclusive interpretation – leaving a continuous data spine.

**psaltery
.
.
.
*-
**psaltery
.
*-


In addition, rid provides a -t option which removes “duplicate” or repeated tandem interpretations. In the above example there is no need to repeat the meter signature and key signature in each measure. The following command will concatenate each of the three measures together, and then eliminate the unwanted interpretations:

cat bar1 bar2 bar3 | rid -ut


The resulting output is given below:

!! File 1
**kern  **kern  **kern  **kern
*M4/4   *M4/4   *M4/4   *M4/4
*k[f#]  *k[f#]  *k[f#]  *k[f#]
4GG     4B      4d      4g
=1      =1      =1      =1
4GG     4d      4g      4b
4FF#    4d      [4a     4dd
8GG     4d      8a]     4b
8BB     .       [8g     .
4D      4d      8g]     4a
.       .       8f#     .
!! File 2
=2      =2      =2      =2
8G      4d      4g      4b
8F#     .       .       .
4E      4e      4g      4cc#
4D;     4A;     4f#;    4dd;
8A      4cn     4a      4ee
8G      .       .       .
!! File 3
=3      =3      =3      =3
4F#     4d      [4a     8dd
.       .       .       8cc
4G      4d      8a]     4b
.       .       [8g     .
8C#     8e      8g]     4a
8D#     4B      8f#     .
8E      .       8e      8g
8F#     8A      8d      8a
*-      *-      *-      *-


Of course care should be exercised when concatenating inputs together. Although an output may conform to the Humdrum syntax, the result can nevertheless violate conventions for a specific representation such as  **kern. For example, if we were to concatenate measure 85 to measure 87, it is possible that tied-notes won’t match up, or that phrases will begin without ending, etc. These anomalies may cause problems with subsequent processing.

## Assembling Parts Using the assemble Command¶

Assembling parts into a full score is slightly more challenging than concatenating together musical segments. The principle tool for joining spines together is the assemble command. Consider the following two files:

!! Assemble example
!! File 1
**Letters
! A to E
A
B
C
D
E
*-

!! Assemble example
!! File 2
**Numbers
! 1 to 5
1
2
3
4
5
*-


These two files can be aligned side by side using assemble :

assemble letters numbers


The resulting output is:

!! Assemble example
!! File 1
!! File 2
**Letters       **Numbers
! A to E        ! 1 to 5
A       1
B       2
C       3
D       4
E       5
*-      *-


Note the following: (1) The spines are joined side by side from left to right in the same order as specified on the command line. (2) Local comments are preserved in their appropriate spines. (3) When identical global comments occur at the same location in both files, only a single instance of the comment is output. (4) Dissimilar global comments are output successively.

The files joined by assemble are not confined to a single spine. For example, one input file may contain 2 spines and a second file may contain 20 spines. The resulting output will contain 22 spines. There is no limit to the number of files that can be assembled at one time.

In many cases, the input files will have dissimilar lengths. The assemble command will correctly terminate the appropriate spines. For example, in the above case, if the numbers file contained only the numbers 1 to 3, the assembled output would appear as follows:

!! Assemble example
!! File 1
!! File 2**Letters**Numbers
! A to E! 1 to 3
A1
B2
C3
**-
D
E
*-


If the order of the input files was reversed, assemble would produce an output with the appropriate spine-path changes:

!! Assemble example
!! File 2
!! File 1
**Numbers       **Letters
! 1 to 3        ! A to E
1       A
2       B
3       C
*       -*
D
E
*-


Note that if all of the input files conform to the Humdrum syntax, then assemble guarantees that the assembled output will also conform to the Humdrum syntax.

## Aligning Durations Using the timebase Command¶

Suppose now that we wanted to join two hypothetical files containing  **kern_ data. The first file contains two quarter notes, whereas the second file contains four eighth notes:

!! File 1
**kern
4c
4d
*-

!! File 2
**kern
8e
8g
8f
8g
*-


Using assemble to paste them together will clearly lead to an uncoordinated result. The two quarter notes in file 1 will be incorrectly matched with the first two eighth notes in file 2.

The Humdrum timebase command can be used to reformat either  **kern_ or **recip inputs so that each output data record represents an equivalent slice (elapsed duration) of time. (Barlines are ignored by timebase .) The timebase command achieves this by padding an input with null data records. In the above case, we would preprocess file 1 as follows:

timebase -t 8 file1 > file1.tb


The new file would look like this:

!! File 1
**kern
4c
.
4d
.
*-


The -t option is used to indicate the “time base” – in this case, an eighth duration. Since all non-barline data records in both files represent elapsed durations of an eighth-note, we can continue by using the assemble command as before. The command:

assemble file1.tb file2


will result in the following two-part score:

!! File 1
!! File 2
**kern  **kern
4c      8e
.       8g
4d      8f
.       8g
*-      *-


Suppose that file2 also contained a quarter-note. For example, consider a revised file2:

!! File 2
**kern
8e
8g
4f
*-


Before assembling the two parts, we would need to apply the timebase command to this file (using the same 8th-note time-base value). Assembling the two “time-based” files would produce the following result:

!! File 1
!! File 2
**kern  **kern
4c      8e
.       8g
4d      4f
.       .
*-      *-


Notice that we have a spurious null data record in the last line; both parts encode null tokens. For most processing, the presence of null data records is inconsequential. However, if we wish, these null data records can be eliminated using the rid command with the -d option. In fact it is common to follow an assemble command with rid -d to strip away unnecessary null data records. The command:

assemble file1.tb file2.tb | rid -d


would result in the following output:

!! File 1
!! File 2
**kern  **kern
4c      8e
.       8g
4d      4f
*-      *-


The timebase command can be applied to multi-spine inputs as well as single-spine inputs. Consider, the following input:

**kern  **kern  **kern  **kern  **commentary
4g      8.r     8.cc    16ee    2nd inversion
.       .       .       8ff     .
.       32b     16cc    16gg    clash
.       32a     .       .       .
8f      8cc     8dd     8ff     suspension
*-      *-      *-      *-      *-


The following command will cause the addition of null data records so that each data record represents an elapsed time of a 32nd duration. Incidentally, notice that any spine contain non-rhythmic data – such as the  **commentary spine in the above example – is also transformed so that synchronous data is maintained.

timebase -t 32 Corelli


The corresponding output is as follows.

**kern  **kern  **kern  **kern  **commentary
*tb32   *tb32   *tb32   *tb32   *tb32
4g      8.r     8.cc    16ee    2nd inversion
.       .       .       .       .
.       .       .       8ff     .
.       .       .       .       .
.       .       .       .       .
.       .       .       .       .
.       32b     16cc    16gg    clash
.       32a     .       .       .
8f      8cc     8dd     8ff     suspension
.       .       .       .       .
.       .       .       .       .
.       .       .       .       .
*-      *-      *-      *-      *-


Notice that timebase has added a tandem interpretation (*tb32). This indicates that the output has been processed so that each non-barline data record represents an elapsed duration equivalent to a thirty-second note.

## Assembling N-tuplets¶

Typically, one can simply use the shortest duration present as a guide for a suitable time-base value. The shortest duration can be determined using the census-k command described in Chapter 4. However, tuplets require a little more sophistication. Suppose we wanted to assemble two parts, one containing just eighth-notes and the other containing just quarter-note triplets. (The quarter-note triplets will be encoded as three notes in the time of a half-note, or “6th” notes.) We need to create an output whose rhythmic structure will appear as follows:

**kern  **kern
*M2/4   *M2/4
=1      =1
6       8
.       8
6       .
.       8
6       .
.               8
=2      =2
6       8
.       8
6       .
.       8
6       .
.       8
=3      =3
*-      *-


In this case, choosing a time-base according to the shortest duration (8th) will not work since a 6th note is not an integral multiple of the eighth duration. We need to find a common duration factor for both values. The shortest common duration is a 24th note (there are three 24th notes in an 8th note, and four 24th notes in a 6th note). Applying the time-base value 24’ to both files will allow us to coordinate them properly. Remember that rid -d can be used to eliminate unnecessary null data records once we have finished assembling the spines. In the worst case, you can determine a common duration factor by simply multiplying together the shortest notes in the files to be assembled. For example, 6 x 8 = 48; so a time-base of 48 will be guaranteed to work for both files.

## Checking an Assembled Score Using proof¶

In assembling any score from a set of parts, there is always the danger of using the wrong time-base value. When parts are miscoordinated, it is typically the consequence of one or more notes being discarded by timebase . Fortunately, such miscoordinations are easily detected by applying the proof command to any assembled **kern output. The proof utility checks **kern representations for a wide variety of possible encoding errors or ambiguities:

proof fullscore


By way of summary, creating a full score from a set of **kern parts involves the following five tasks: (1) Identify a common duration factor for all the parts. Use census to determine the shortest duration; if any of the parts contains an N-tuplet, then the common duration factor may be smaller than the shortest note. (2) Use the timebase command to expand each input file separately using the common duration factor. (3) Assemble the parts using assemble. (4) If desired, eliminate unnecessary null data records usingi **rid -d**._ (5) Check the assembled score for rhythmic coherence using the proof command.

## Other Uses for the timebase Command¶

The most common use of timebase is as a way of expanding a file by padding it with null data records. However, timebase can also be used to contract a file, giving us only those sonorities that lie a fixed duration apart. For example, specifying a time-base of -t 2 will cause only those sonorities that are separated by a half-note duration to be output. This sort of rhythmic reduction can be useful in certain circumstances. For example, suppose you suspect there may be a hemiola tendency in a given work by Brahms, where the duration separating hemiola notes is a dotted-quarter. The command:

timebase -t 4. brahms


can be used to extract only those sonorities that are separated by a dotted- quarter duration.

Similarly, suppose we want to extract all sonorities falling on the third beat of a waltz written in 3/2 meter. First we would edit the input file so it begins on the third beat of some measure. Then we could use the following command:

grep -v ^= waltz | timebase -t 1. > 3rd_beat


Note that the use of grep here is essential in order to eliminate barlines. The timebase command resets itself with each barline, so time- base durations are calculated from the beginning of the bar. When barlines are eliminated, timebase cannot synchronize to the beginning of each bar and so simply floats along at the fixed time-base.

## Additional Uses of assemble and timebase¶

Although we normally assemble parts together, sometimes it is useful to assemble entire scores together. Suppose we wanted to listen to a theme at the same time as one of its variations. We might first use yank to extract the appropriate sections. At the same time we might determine a common duration factor and expand them using timebase .

yank -s Theme -r 1 blacksmith | timebase -t 32 > temp1
yank -s 'Variation 1' -r 1 blacksmith | timebase -t 32 > temp2


Then we assemble the two sections together, translate to the **MIDI representation and use perform to listen to both sections at the same time:

assemble temp1 temp2 | midi | perform


Similarly, suppose we would like to compare the bass lines for each variation in some set. We might extract each of the bass lines, assemble them into a single score, and then use the |ms|_ and**ghostview**commands to allow us to see all of the bass lines for all of the variations concurrently.

yank -s 'Variation 1' -r 1 goldberg | timebase -t 16 > temp1
yank -s 'Variation 2' -r 1 goldberg | timebase -t 16 > temp2


etc. ...

assemble temp1 temp2 temp3 ... | rid -d | ms > basslines.ps
ghostview basslines.ps


The most common use of assemble is not to assemble parts, but to assemble different types of concurrent information. Suppose we would like to determine whether descending minor seconds are more likely to be fa-mi rather than do-ti. We can use the mint command to characterize melodic intervals, and the solfa command to characterize scale degrees. Assume that our input is monophonic:

mint melodies > temp1
solfa melodies > temp2


The files temp1 and temp2 will have the same length, so we can assemble them together. This will generate an output consisting of two spines, **mint and **solfa. In effect, the **mint spine data will tell us the interval used to approach the scale degree encoded in the **solfa spine. We can use grep to search for the appropriate combinations of interval and scale degree and count the number of occurrences:

assemble temp1 temp2 | grep -c '-m2.*mi'
assemble temp1 temp2 | grep -c '-m2.*ti'


This same approach can be used to address (innumerable) questions pertaining to concurrent patterns. For example, suppose we have a **harm spine that identifies the Roman numeral’ functional harmony for some choral work. We can identify complex situations such as the following: for the soprano voice, count how many subdominant pitches are approached by an interval of a rising third or a rising sixth and coincide with a dominant seventh chord. First, let’s extract the soprano line and create a corresponding scale degree representation using |deg|_ . We can use the -a option to avoid outputting the melodic direction signifiers (^ and v):

extract -i '*Isopran' howells | deg -a > temp1


Next, let’s again extract the soprano voice and create a corresponding melodic interval representation using mint . Since we are not interested in interval qualities we can invoke the -d option to output only diatonic interval sizes.

extract -i '*Isopran' howells | mint -d > temp2
extract -i '**harm' howells > temp3


We have also extracted the **harm spine and placed it in the file  temp3. If we assemble together our three temporary files, the result will have three spines: **deg, **mint and **harm. We can now use grep to search and count all instances of subdominant pitches that are approached by ascending thirds/sixths and that coincide with dominant seventh chords (in the **kern representation: V7‘):

assemble temp1 temp2 temp3 | grep -c '^4**+[36]**V7


The timebase command can also be used for tasks other than assembling parts together. Suppose we would like to determine whether secondary dominant chords are more likely to appear on the third beat than other beats in a triple meter work. The timebase command can be used to reformat a score so that each measure occupies the same number of data records. For example, in a 3/4 meter, an eighth-note time-base will mean that each measure will contain six data records, and the fifth data record will correspond to the onset of the third beat. Recall from Chapter 12 that the yank -m command allows us to extract particular data records following a specified marker. In the following command, we have defined the marker as a barline (-m ^=) and instructed yank to fetch the fifth line following each occurrence of the marker (-r 5). In our example, the grep command is being used to count V/V chords occurring on third beats:

 timebase -t 8 strauss | solfa | yank -m = -r 5 | grep re > >> | grep fe | grep -c la`

We can repeat this command for beats one and two by changing the -r parameter to 1 and 3 respectively.

## Reprise¶

In this chapter we have learned how to concatenate musical passages together using the cat command. We also learned how to eliminate redundant exclusive and tandem interpretations from concatenated outputs using the -u and -t options for rid. In addition, we learned how to assemble two or more spines into a single output file using assemble. In the case of **kern and **recip representations, we learned how to use the timebase command to preprocess each constituent file so that all data records represent equivalent elapsed durations. Having assembled a full score from parts, rid -d can be used to eliminate any residual or unnecessary null data records. The proof command can be used to ensure that any assembled **kern data is correctly aligned.

Finally, we learned that the timebase command can be used for other analytic purposes. Specifically, it can be used to reduce a score rhythmically so only particular onset points or beats are retained. In Chapter 23 we will see additional uses of timebase for a variety of types of rhythmic tasks.