Why is the attached code duplicating the column names? What improved python code can I use without duplication of the column names? How can I also include the filename as a column?
Why is the attached code duplicating the column names? What improved python code can I use without duplication of the column names? How can I also include the filename as a column?
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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Question
Why is the attached code duplicating the column names? What improved python code can I use without duplication of the column names? How can I also include the filename as a column?
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1 # loop through the audio files in the directory
2 for filename in os.listdir(audio_dir):
1234567890
if filename.endswith(".wav"):
#load the audio file and extract its chroma features
y, sr=librosa.load(os.path.join(audio_dir, filename))
655
10
11
12
13
14
15
16
labels.append(labels_flat)
17 #convert the list of chroma features and labels to a dataframe
18 df=pd.DataFrame(chroma_features, columns=labels)
19 #print the data frame
20 print (df.head())
21
/Users/mac/opt/anaconda3/lib/python3.9/site-packages/librosa/util/decorators.py:88: UserWarning: T
U U U U U U
uning from empty frequency set.
return f(*args, **kwargs)
с
с
с
chroma=librosa.feature.chroma_stft (y=y,sr=sr)
#calculate the mean of each chroma feature
chroma_means=np.mean(chroma, axis=1)
#label the chroma features with their 12-tonal variations
chroma_labels=["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]
#flatten the chroma features and their labels
chroma_flat=chroma_means.reshape(-1)
C
с
labels_flat=[f"{label}" for label in chroma_labels]
#Append the flattened chroma features and labels to the list
chroma_features.append(chroma_flat)
с
******
C#
C#
C#
C#
C#
C#
AAAAAA
D
D
D
D
D
******
DEEDDE
F
F
F
F
F
F
F#
**
F#
F#
F#
F#
F#
1](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F7eebb740-a373-4c85-98d2-7b97f64873d1%2F2e93d12b-afe5-472d-b785-84fb6eb62984%2Frrb3a2_processed.png&w=3840&q=75)
Transcribed Image Text:=
1 # loop through the audio files in the directory
2 for filename in os.listdir(audio_dir):
1234567890
if filename.endswith(".wav"):
#load the audio file and extract its chroma features
y, sr=librosa.load(os.path.join(audio_dir, filename))
655
10
11
12
13
14
15
16
labels.append(labels_flat)
17 #convert the list of chroma features and labels to a dataframe
18 df=pd.DataFrame(chroma_features, columns=labels)
19 #print the data frame
20 print (df.head())
21
/Users/mac/opt/anaconda3/lib/python3.9/site-packages/librosa/util/decorators.py:88: UserWarning: T
U U U U U U
uning from empty frequency set.
return f(*args, **kwargs)
с
с
с
chroma=librosa.feature.chroma_stft (y=y,sr=sr)
#calculate the mean of each chroma feature
chroma_means=np.mean(chroma, axis=1)
#label the chroma features with their 12-tonal variations
chroma_labels=["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]
#flatten the chroma features and their labels
chroma_flat=chroma_means.reshape(-1)
C
с
labels_flat=[f"{label}" for label in chroma_labels]
#Append the flattened chroma features and labels to the list
chroma_features.append(chroma_flat)
с
******
C#
C#
C#
C#
C#
C#
AAAAAA
D
D
D
D
D
******
DEEDDE
F
F
F
F
F
F
F#
**
F#
F#
F#
F#
F#
1
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