My abuela has always been known as the best cook in the family. No matter what she is cooking--arroz con pollo, lasagna, or my personal favorite, crumb cake, everyone always enjoys. Abuela shows her love through her food. Unfortunately this Easter season my abuela was not with us as she resides in North Carolina currently. Despite the distance, my family and I were still able to have a piece of her there with us through her shared recipes. This video demonstrates the process of making my favorite recipe I've learned from her, as well as share her story of how she came to love cooking so much and how it means so much to both my family and I.
Sunday, April 3, 2016
Sunday, February 28, 2016
Sampling Suburban Station
The public space in Philadelphia that I sampled was Suburban Station, a train station in Center City. As a center for public transportation, it inevitably has peak and off-peak hours. My intention for this sampling project was to explore the station at the same time on a weekday and a weekend in order to compare the two.
In order to compile my data set, I arrived at Suburban Station at 5 PM on both Friday and Saturday. I gathered data for a half hour, leaving at 5:30 PM each day. Two methods were used to collect data. The first and more extensive method was the use of a video camera and tripod, both of which were rented from the communications department. I sat at a bench with the camera set up next to me facing an area where the entrance to a track could be seen, as well as a long aisle. The intention was to record from the same angle both days, however on Saturday the bench was occupied and I thus adjusted my method by sitting at a bench across from the original, but capturing the same aisle. Once set up, I recorded for a half hour. My intention with this method was to create a time-lapse video with the speed fast-forwarded, as I believed this would best demonstrate the volume of people in the station. The second method of data collection involved an audio recording. Halfway through being in the station, I recorded an audio file on my iPhone for five minutes. Audio was chosen as a means of collection because the fast-forwarded time lapse would alter the sound of the video, and I wished to add sound to capture another layer of what the station was truly like.
Video and the audio file from Friday, February 26, can be seen below:
Looking at the video from the weekday, Friday, February 26, the station was very crowded. People could be seen walking by often, typically at a fast pace. The video also showed many people waiting at the entrance to the track. The attire of many of the people in the video was professional. Many were carrying briefcases or other bags. Women’s outfits varied, but the majority of men wore buttoned shirts and dress pants. The youngest of individuals captured by the video appeared to be of teenage years, but there was a preponderance of people that appeared above the age of 18. The data predominantly showed people waiting for their train or walking around the station. The sound clip from the same date was mainly comprised of background music that played throughout the station. This music was interspersed with train arrival announcements, three of which can be heard in the five-minute clip alone. Some chatter can be heard in the audio recording, but not an overwhelming amount.
Video and the audio file from Saturday, February 27, can be seen below:
According to the video footage of the weekend, Saturday, February 27, the station did not contain as many people as it had the previous day. People could still be seen walking in the aisles, however this was done at a slower pace than those walking on Friday. The attire of the people seen in the video varied greatly, however it could be categorized as much less formal than the earlier video. Very few briefcases were seen during this data collection, and many more shopping bags were witnessed. Ages were more variant in this set of data, with the youngest person being a very small child. Despite this variance, the majority of the people in the station appeared to be above the age of 18. Saturday’s sound clip did not vary much from Friday, despite the noticeable difference in the amount of people present. The background music contributed to most of the sound, and less train arrival announcements were made, with only one recorded in the clip compared to the three on Friday.
Overall, the data collected demonstrated that Suburban Station was more crowded on this particular weekday, and that people tended to dress more casually on this particular weekend. Prior to collecting this data, I inevitably held some assumptions about what I would find during my collection process. The difference in volume of people as well as their dress and activities was essentially what I assumed heading into this project. I was surprised, however, at the lack of difference in the audio files that were collected. With the amount of people present, I expected a weekday to be louder. Without the train announcements, which suggested that more trains run on a weekday than the weekend, there was very little difference. In the end, this project adds to data describing Suburban Station, and future projects could examine the station on various weekdays, as well as including multiple camera angles for video collection.
In order to compile my data set, I arrived at Suburban Station at 5 PM on both Friday and Saturday. I gathered data for a half hour, leaving at 5:30 PM each day. Two methods were used to collect data. The first and more extensive method was the use of a video camera and tripod, both of which were rented from the communications department. I sat at a bench with the camera set up next to me facing an area where the entrance to a track could be seen, as well as a long aisle. The intention was to record from the same angle both days, however on Saturday the bench was occupied and I thus adjusted my method by sitting at a bench across from the original, but capturing the same aisle. Once set up, I recorded for a half hour. My intention with this method was to create a time-lapse video with the speed fast-forwarded, as I believed this would best demonstrate the volume of people in the station. The second method of data collection involved an audio recording. Halfway through being in the station, I recorded an audio file on my iPhone for five minutes. Audio was chosen as a means of collection because the fast-forwarded time lapse would alter the sound of the video, and I wished to add sound to capture another layer of what the station was truly like.
Video and the audio file from Friday, February 26, can be seen below:
Looking at the video from the weekday, Friday, February 26, the station was very crowded. People could be seen walking by often, typically at a fast pace. The video also showed many people waiting at the entrance to the track. The attire of many of the people in the video was professional. Many were carrying briefcases or other bags. Women’s outfits varied, but the majority of men wore buttoned shirts and dress pants. The youngest of individuals captured by the video appeared to be of teenage years, but there was a preponderance of people that appeared above the age of 18. The data predominantly showed people waiting for their train or walking around the station. The sound clip from the same date was mainly comprised of background music that played throughout the station. This music was interspersed with train arrival announcements, three of which can be heard in the five-minute clip alone. Some chatter can be heard in the audio recording, but not an overwhelming amount.
Video and the audio file from Saturday, February 27, can be seen below:
According to the video footage of the weekend, Saturday, February 27, the station did not contain as many people as it had the previous day. People could still be seen walking in the aisles, however this was done at a slower pace than those walking on Friday. The attire of the people seen in the video varied greatly, however it could be categorized as much less formal than the earlier video. Very few briefcases were seen during this data collection, and many more shopping bags were witnessed. Ages were more variant in this set of data, with the youngest person being a very small child. Despite this variance, the majority of the people in the station appeared to be above the age of 18. Saturday’s sound clip did not vary much from Friday, despite the noticeable difference in the amount of people present. The background music contributed to most of the sound, and less train arrival announcements were made, with only one recorded in the clip compared to the three on Friday.
Overall, the data collected demonstrated that Suburban Station was more crowded on this particular weekday, and that people tended to dress more casually on this particular weekend. Prior to collecting this data, I inevitably held some assumptions about what I would find during my collection process. The difference in volume of people as well as their dress and activities was essentially what I assumed heading into this project. I was surprised, however, at the lack of difference in the audio files that were collected. With the amount of people present, I expected a weekday to be louder. Without the train announcements, which suggested that more trains run on a weekday than the weekend, there was very little difference. In the end, this project adds to data describing Suburban Station, and future projects could examine the station on various weekdays, as well as including multiple camera angles for video collection.
Thursday, February 11, 2016
Sunday, February 7, 2016
Playground Song
When contemplating what should be
the source of my sound for this song project, I ultimately decided on the idea
of collecting sound from a playground. As a place where a lot of joy and
excitement occurs, I thought this could result in a uplifting song. A
playground also has a lot of toys and different objects that produce sound,
which would allow for an impactful song. In order to collect the sound, I went
to a local playground and recorded various sounds from objects located there.
After collecting sounds, I decided
that I wanted to make my song more of a story about going to the park. Thus, I
started the song off in GarageBand with the noise of car keys, car unlocking,
door closing and engine starting to give the impression of jumping in the car
to head to the playground to the audience. I collected all of these noises from
my own car. Following this, I chose one of the preexisting loops on GarageBand
to serve as the base to the song.
I
chose Eastern Gold Oud because I enjoyed the beat and felt that it would fit
well with the sounds that I had collected from the playground. As I wanted to
create a gradual build into the base of the song, I used the volume tool in
order to create a fade in effect.
Once I had the
base of the song established, I then went to work adding in found sound. I knew
that I wanted to include a basketball dribbling noise, as it is one that is
common on the blacktop on the playground and would add a good beat to the song,
almost sounding like drums. Because I did not own a basketball, I found a
basketball sound from Freesounds.org, which hosts creative-commons licensed
sound. The clip was short at around five seconds, so I made the dribble into a
loop so that it would play throughout the song.
Following this, I layered in a recording
of claps that I had made at the playground, reminiscent of playing various hand
clap games when I was a kid at recess. I inserted these claps to match the beat
of the song. Ultimately, I also added a sound of chains from swings jingling
behind alternating clap noises, as it created an almost echo effect which I
found interesting.
The park that I
collected sound at was unique in that its playground included a lot of chime
and bell toys. While there, I recorded the sound of a chime being hit. A lot of
the noises within my song at this point were lower and louder, so I thought
that adding the delicate, higher noise of the chime would provide nice
contrast. The song repeated from there. Finally, I again faded out the sound
and included the key jingling, car unlocking, door shutting, and engine
starting noise at the end in order to signify the end of a day at the
playground and the end of the song.
Sunday, January 24, 2016
Sampling
While
reading the “How Music Sampling Works” article, I was reminded of a documentary
that I had previously watched in my Ethics in Communications Course entitled,
“A Remix Manifesto.” Both pieces demonstrate the long and complicated history
behind sampling and how it can truly maximize creativity while resulting in
legal problems. The main issue that stood out to me was that when there is a
legal dispute regarding sampling, the compensation tends to go to big companies
that technically hold the copyright, rather than the artists themselves who
were actually involved in the process of creating the music and who seem to be
more rightfully entitled to the earnings. It seems as if copyright lawsuits are
just another way for large companies to increase their revenue, which can
stifle the creativity of others.
The
article, “10 Incredible Ways Electronic Artists Are Using Found Sound” was one
that was very interesting to me. Prior to reading this, when I thought of
sampling, I thought of using musical pieces of other artists within ones work.
The concept of found sound such as Diego Stocco using the sounds in a dry
cleaner had never occurred to me before as constituting sampling. This
broadened my perception to see that there are many ways in which sampling can be
used. The Avalanches “Frontier Scientist” especially struck me. Again, I never
thought using spoken work recordings could be incorporated as sampling. This
has got me to start thinking about my project. I am thinking of going to a
playground to collect my found sound as I think there are a variety of noises
that could be recorded there, such as a bouncing basketball, the sound of
swings and woodchips to make a dynamic sound.
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